diff --git a/opennlp-docs/pom.xml b/opennlp-docs/pom.xml
index 9d6c656e3..444972311 100644
--- a/opennlp-docs/pom.xml
+++ b/opennlp-docs/pom.xml
@@ -31,46 +31,153 @@
   <artifactId>opennlp-docs</artifactId>
   <packaging>pom</packaging>
   <name>Apache OpenNLP :: Documentation</name>
-  
-  <build>
-    <plugins>
-	<plugin>
-		<groupId>com.agilejava.docbkx</groupId>
-		<artifactId>docbkx-maven-plugin</artifactId>
-		<version>2.0.17</version>
-		<executions>
-          <execution>
-            <goals>
-              <goal>generate-html</goal>
-            </goals>
-            <phase>package</phase>
-          </execution>
-        </executions>
-		<dependencies>
-			<dependency>
-			  <groupId>org.docbook</groupId>
-			  <artifactId>docbook-xml</artifactId>
-			  <version>4.4</version>
-			</dependency>
-       	</dependencies>
-		<configuration>
-			<xincludeSupported>true</xincludeSupported>
-			<includes>opennlp.xml</includes>
-			<htmlStylesheet>css/opennlp-docs.css</htmlStylesheet>
-			<highlightSource>1</highlightSource>
-			<htmlCustomization>${project.basedir}/src/main/resources/xsl/html.xsl</htmlCustomization> 
-			<postProcess>
-				<copy todir="${project.build.directory}/docbkx/html">
-					<fileset dir="${project.basedir}/src/docbkx">
-						<include name="**/*.css" />
-						<include name="**/*.png" />
-						<include name="**/*.gif" />
-						<include name="**/*.jpg" />
-					</fileset>
-				</copy>
-			</postProcess>
-		</configuration>
-	</plugin>
-    </plugins>
-  </build>
+
+	<profiles>
+		<profile>
+			<id>doc-manual-html</id>
+			<activation>
+				<activeByDefault>true</activeByDefault>
+			</activation>
+			<build>
+				<plugins>
+					<plugin>
+						<groupId>com.agilejava.docbkx</groupId>
+						<artifactId>docbkx-maven-plugin</artifactId>
+						<executions>
+							<execution>
+								<id>doc-manual-html</id>
+								<goals>
+									<goal>generate-html</goal>
+								</goals>
+								<phase>package</phase>
+							</execution>
+						</executions>
+						<configuration>
+							<htmlStylesheet>css/opennlp-dev-manual.css</htmlStylesheet>
+							<highlightSource>1</highlightSource>
+							<htmlCustomization>${project.basedir}/src/main/resources/xsl/html.xsl</htmlCustomization>
+							<postProcess>
+								<copy todir="${project.build.directory}/docbkx/html">
+									<fileset dir="${project.basedir}/src/docbkx">
+										<include name="**/*.css" />
+										<include name="**/*.png" />
+										<include name="**/*.gif" />
+										<include name="**/*.jpg" />
+									</fileset>
+								</copy>
+							</postProcess>
+						</configuration>
+					</plugin>
+				</plugins>
+			</build>
+		</profile>
+		<profile>
+			<id>doc-manual-pdf</id>
+			<activation>
+				<activeByDefault>false</activeByDefault>
+			</activation>
+			<build>
+				<plugins>
+					<plugin>
+						<groupId>com.agilejava.docbkx</groupId>
+						<artifactId>docbkx-maven-plugin</artifactId>
+						<executions>
+							<execution>
+								<id>dev-manual-pdf</id>
+								<goals>
+									<goal>generate-pdf</goal>
+								</goals>
+								<phase>package</phase>
+							</execution>
+						</executions>
+						<configuration>
+							<foCustomization>${project.basedir}/src/main/resources/xsl/pdf.xsl</foCustomization>
+							<highlightSource>1</highlightSource>
+							<hyphenate>true</hyphenate>
+							<hyphenateVerbatim>false</hyphenateVerbatim>
+							<showXslMessages>false</showXslMessages>
+						</configuration>
+					</plugin>
+				</plugins>
+			</build>
+		</profile>
+		<profile>
+			<id>doc-manual-epub</id>
+			<activation>
+				<activeByDefault>false</activeByDefault>
+			</activation>
+			<build>
+				<plugins>
+					<plugin>
+						<groupId>com.agilejava.docbkx</groupId>
+						<artifactId>docbkx-maven-plugin</artifactId>
+						<executions>
+							<execution>
+								<id>dev-manual-epub</id>
+								<goals>
+									<goal>generate-epub3</goal>
+								</goals>
+								<phase>package</phase>
+							</execution>
+						</executions>
+						<configuration>
+							<hyphenate>true</hyphenate>
+							<hyphenateVerbatim>false</hyphenateVerbatim>
+							<htmlStylesheet>css/opennlp-dev-manual.css</htmlStylesheet>
+							<epub3Customization>${project.basedir}/src/main/resources/xsl/epub3.xsl</epub3Customization>
+							<chunkQuietly>true</chunkQuietly>
+							<showXslMessages>false</showXslMessages>
+							<preProcess>
+								<copy todir="${project.build.directory}/docbkx/epub3/images">
+									<fileset dir="src/docbkx/images/">
+										<include name="*.png"/>
+									</fileset>
+								</copy>
+								<copy todir="${project.build.directory}/docbkx/epub3/css"
+											file="src/docbkx/css/opennlp-dev-manual.css"/>
+							</preProcess>
+						</configuration>
+					</plugin>
+				</plugins>
+			</build>
+		</profile>
+	</profiles>
+
+	<build>
+		<pluginManagement>
+			<plugins>
+				<plugin>
+					<groupId>com.agilejava.docbkx</groupId>
+					<artifactId>docbkx-maven-plugin</artifactId>
+					<version>2.0.17</version>
+					<dependencies>
+						<dependency>
+							<groupId>net.sf.docbook</groupId>
+							<artifactId>docbook-xml</artifactId>
+							<version>5.0-all</version>
+							<classifier>resources</classifier>
+							<type>zip</type>
+							<scope>runtime</scope>
+						</dependency>
+						<dependency>
+							<groupId>net.sf.xslthl</groupId>
+							<artifactId>xslthl</artifactId>
+							<version>2.0.1</version>
+							<scope>runtime</scope>
+						</dependency>
+						<dependency>
+							<groupId>net.sf.offo</groupId>
+							<artifactId>fop-hyph</artifactId>
+							<version>1.2</version>
+							<scope>runtime</scope>
+						</dependency>
+					</dependencies>
+					<configuration>
+						<xincludeSupported>true</xincludeSupported>
+						<includes>opennlp.xml</includes>
+					</configuration>
+				</plugin>
+			</plugins>
+		</pluginManagement>
+	</build>
 </project>
diff --git a/opennlp-docs/src/docbkx/chunker.xml b/opennlp-docs/src/docbkx/chunker.xml
index 444819926..836d9535a 100644
--- a/opennlp-docs/src/docbkx/chunker.xml
+++ b/opennlp-docs/src/docbkx/chunker.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -43,14 +43,12 @@ under the License.
 		</para>
 		<para>
         <screen>
-				<![CDATA[
-$ opennlp ChunkerME en-chunker.bin]]>
+<![CDATA[$ opennlp ChunkerME en-chunker.bin]]>
 		</screen>
 		The Chunker now reads a pos tagged sentence per line from stdin.
 		Copy these two sentences to the console: 
 		<screen>
-				<![CDATA[
-Rockwell_NNP International_NNP Corp._NNP 's_POS Tulsa_NNP unit_NN said_VBD it_PRP signed_VBD 
+<![CDATA[Rockwell_NNP International_NNP Corp._NNP 's_POS Tulsa_NNP unit_NN said_VBD it_PRP signed_VBD 
     a_DT tentative_JJ agreement_NN extending_VBG its_PRP$ contract_NN with_IN Boeing_NNP Co._NNP
     to_TO provide_VB structural_JJ parts_NNS for_IN Boeing_NNP 's_POS 747_CD jetliners_NNS ._.
 Rockwell_NNP said_VBD the_DT agreement_NN calls_VBZ for_IN it_PRP to_TO supply_VB 200_CD
@@ -58,8 +56,7 @@ Rockwell_NNP said_VBD the_DT agreement_NN calls_VBZ for_IN it_PRP to_TO supply_V
 		</screen>
 		The Chunker will now echo the sentences grouped tokens to the console:
 				<screen>
-				<![CDATA[
-[NP Rockwell_NNP International_NNP Corp._NNP ] [NP 's_POS Tulsa_NNP unit_NN ] [VP said_VBD ]
+<![CDATA[[NP Rockwell_NNP International_NNP Corp._NNP ] [NP 's_POS Tulsa_NNP unit_NN ] [VP said_VBD ]
     [NP it_PRP ] [VP signed_VBD ] [NP a_DT tentative_JJ agreement_NN ] [VP extending_VBG ]
     [NP its_PRP$ contract_NN ] [PP with_IN ] [NP Boeing_NNP Co._NNP ] [VP to_TO provide_VB ]
     [NP structural_JJ parts_NNS ] [PP for_IN ] [NP Boeing_NNP ] [NP 's_POS 747_CD jetliners_NNS ] ._.
@@ -73,12 +70,11 @@ Rockwell_NNP said_VBD the_DT agreement_NN calls_VBZ for_IN it_PRP to_TO supply_V
 		<section id="tools.parser.chunking.api">
 		<title>Chunking API</title>
 		<para>
-		    The Chunker can be embedded into an application via its API.
+			The Chunker can be embedded into an application via its API.
 			First the chunker model must be loaded into memory from disk or another source.
 			In the sample below it is loaded from disk.
 			<programlisting language="java">
-				<![CDATA[
-InputStream modelIn = null;
+<![CDATA[InputStream modelIn = null;
 ChunkerModel model = null;
 
 try (modelIn = new FileInputStream("en-chunker.bin")){
@@ -87,8 +83,7 @@ try (modelIn = new FileInputStream("en-chunker.bin")){
 			</programlisting>
 			After the model is loaded a Chunker can be instantiated.
 			<programlisting language="java">
-				<![CDATA[
-ChunkerME chunker = new ChunkerME(model);]]>
+<![CDATA[ChunkerME chunker = new ChunkerME(model);]]>
 			</programlisting>
 			The Chunker instance is now ready to tag data. It expects a tokenized sentence
 			as input, which is represented as a String array, each String object in the array
@@ -97,8 +92,7 @@ ChunkerME chunker = new ChunkerME(model);]]>
 	   <para>
 	   The following code shows how to determine the most likely chunk tag sequence for a sentence.
 	   	<programlisting language="java">
-		  <![CDATA[
-String[] sent = new String[] { "Rockwell", "International", "Corp.", "'s",
+<![CDATA[String[] sent = new String[] { "Rockwell", "International", "Corp.", "'s",
     "Tulsa", "unit", "said", "it", "signed", "a", "tentative", "agreement",
     "extending", "its", "contract", "with", "Boeing", "Co.", "to",
     "provide", "structural", "parts", "for", "Boeing", "'s", "747",
@@ -116,8 +110,7 @@ String[] tag = chunker.chunk(sent, pos);]]>
 			The confidence scores for the returned tags can be easily retrieved from
 			a ChunkerME with the following method call:
 				   	<programlisting language="java">
-		  <![CDATA[
-double[] probs = chunker.probs();]]>
+<![CDATA[double[] probs = chunker.probs();]]>
 			</programlisting>
 			The call to probs is stateful and will always return the probabilities of the last
 			tagged sentence. The probs method should only be called when the tag method
@@ -129,8 +122,7 @@ double[] probs = chunker.probs();]]>
 			The topKSequences method is capable of returning the top sequences.
 			It can be called in a similar way as chunk.
 			<programlisting language="java">
-		  <![CDATA[
-Sequence[] topSequences = chunk.topKSequences(sent, pos);]]>
+<![CDATA[Sequence[] topSequences = chunk.topKSequences(sent, pos);]]>
 			</programlisting>	
 			Each Sequence object contains one sequence. The sequence can be retrieved
 			via Sequence.getOutcomes() which returns a tags array 
@@ -163,8 +155,7 @@ Sequence[] topSequences = chunk.topKSequences(sent, pos);]]>
 		<para>
 		Sample sentence of the training data: 
 		<screen>
-				<![CDATA[
-He        PRP  B-NP
+<![CDATA[He        PRP  B-NP
 reckons   VBZ  B-VP
 the       DT   B-NP
 current   JJ   I-NP
@@ -192,8 +183,7 @@ September NNP  B-NP
 		<para>
 		    Usage of the tool:
             <screen>
-				<![CDATA[
-$ opennlp ChunkerTrainerME
+<![CDATA[$ opennlp ChunkerTrainerME
 Usage: opennlp ChunkerTrainerME[.ad] [-params paramsFile] [-iterations num] [-cutoff num] \
                -model modelFile -lang language -data sampleData [-encoding charsetName]
 
@@ -217,8 +207,7 @@ Arguments description:
 		en-chunker.train which is encoded as UTF-8. The following command will train the
 		name finder and write the model to en-chunker.bin: 
 		<screen>
-		<![CDATA[
-$ opennlp ChunkerTrainerME -model en-chunker.bin -lang en -data en-chunker.train -encoding UTF-8]]>
+<![CDATA[$ opennlp ChunkerTrainerME -model en-chunker.bin -lang en -data en-chunker.train -encoding UTF-8]]>
 		</screen>
 		Additionally its possible to specify the number of iterations, the cutoff and to overwrite
 		all types in the training data with a single type.
@@ -230,19 +219,17 @@ $ opennlp ChunkerTrainerME -model en-chunker.bin -lang en -data en-chunker.train
                 The Chunker offers an API to train a new chunker model. The following sample code
                 illustrates how to do it:
                 <programlisting language="java">
-                    <![CDATA[
+<![CDATA[
 ObjectStream<String> lineStream =
     new PlainTextByLineStream(new MarkableFileInputStreamFactory(new File("en-chunker.train")), StandardCharsets.UTF_8);
 
 ChunkerModel model;
-
 try(ObjectStream<ChunkSample> sampleStream = new ChunkSampleStream(lineStream)) {
-  model = ChunkerME.train("eng", sampleStream,
-       TrainingParameters.defaultParams(), new ChunkerFactory());
+  	model = ChunkerME.train("eng", sampleStream, TrainingParameters.defaultParams(), new ChunkerFactory());
 }
 
 try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(modelFile))) {
-  model.serialize(modelOut);
+  	model.serialize(modelOut);
 }]]>
                 </programlisting>
             </para>
@@ -260,29 +247,25 @@ try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(model
 		<para>
 		    The following command shows how the tool can be run:
             <screen>
-				<![CDATA[
-$ opennlp ChunkerEvaluator
+<![CDATA[$ opennlp ChunkerEvaluator
 Usage: opennlp ChunkerEvaluator[.ad] -model model [-misclassified true|false] \
                [-detailedF true|false] -lang language -data sampleData [-encoding charsetName]]]>
 		</screen>
 		A sample of the command considering you have a data sample named en-chunker.eval
 		and you trained a model called en-chunker.bin:
             <screen>
-				<![CDATA[
-$ opennlp ChunkerEvaluator -model en-chunker.bin -data en-chunker.eval -encoding UTF-8]]>
+<![CDATA[$ opennlp ChunkerEvaluator -model en-chunker.bin -data en-chunker.eval -encoding UTF-8]]>
 		</screen>
 		and here is a sample output:  
 		<screen>
-		<![CDATA[
-Precision: 0.9255923572240226
+<![CDATA[Precision: 0.9255923572240226
 Recall: 0.9220610430991112
 F-Measure: 0.9238233255623465]]>
 		</screen>
 		You can also use the tool to perform 10-fold cross validation of the Chunker.
 The following command shows how the tool can be run:
         <screen>
-				<![CDATA[
-$ opennlp ChunkerCrossValidator
+<![CDATA[$ opennlp ChunkerCrossValidator
 Usage: opennlp ChunkerCrossValidator[.ad] [-params paramsFile] [-iterations num] [-cutoff num] \
                [-misclassified true|false] [-folds num] [-detailedF true|false] \
                -lang language -data sampleData [-encoding charsetName]
@@ -309,8 +292,7 @@ Arguments description:
 		</screen>
 		It is not necessary to pass a model. The tool will automatically split the data to train and evaluate:
         <screen>
-            <![CDATA[
-$ opennlp ChunkerCrossValidator -lang pt -data en-chunker.cross -encoding UTF-8]]>
+<![CDATA[$ opennlp ChunkerCrossValidator -lang pt -data en-chunker.cross -encoding UTF-8]]>
 		</screen>
 		</para>
 		</section>
diff --git a/opennlp-docs/src/docbkx/cli.xml b/opennlp-docs/src/docbkx/cli.xml
index adc6538d9..7cf7f556e 100644
--- a/opennlp-docs/src/docbkx/cli.xml
+++ b/opennlp-docs/src/docbkx/cli.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -84,8 +84,18 @@ Arguments description:
 </screen> 
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>leipzig</entry>
@@ -113,8 +123,8 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp DoccatEvaluator[.leipzig] -model model [-misclassified true|false] [-reportOutputFile 
-        outputFile] -data sampleData [-encoding charsetName] 
+Usage: opennlp DoccatEvaluator[.leipzig] -model model [-misclassified true|false] [-reportOutputFile
+        outputFile] -data sampleData [-encoding charsetName]
 Arguments description:
 	-model model
 		the model file to be evaluated.
@@ -128,11 +138,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>leipzig</entry>
@@ -160,9 +179,9 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp DoccatCrossValidator[.leipzig] [-misclassified true|false] [-folds num] [-factory factoryName] 
-        [-featureGenerators fg] [-tokenizer tokenizer] [-params paramsFile] -lang language [-reportOutputFile 
-        outputFile] -data sampleData [-encoding charsetName] 
+Usage: opennlp DoccatCrossValidator[.leipzig] [-misclassified true|false] [-folds num] [-factory factoryName]
+        [-featureGenerators fg] [-tokenizer tokenizer] [-params paramsFile] -lang language [-reportOutputFile
+        outputFile] -data sampleData [-encoding charsetName]
 Arguments description:
 	-misclassified true|false
 		if true will print false negatives and false positives.
@@ -186,11 +205,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>leipzig</entry>
@@ -221,11 +249,20 @@ Arguments description:
 Usage: opennlp DoccatConverter help|leipzig [help|options...]
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>leipzig</entry>
@@ -262,7 +299,7 @@ Usage: opennlp DoccatConverter help|leipzig [help|options...]
 Usage: opennlp LanguageDetector model < documents
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.langdetect.LanguageDetectorTrainer'>
@@ -273,8 +310,8 @@ Usage: opennlp LanguageDetector model < documents
 
 <screen>
 <![CDATA[
-Usage: opennlp LanguageDetectorTrainer[.leipzig] -model modelFile [-params paramsFile] [-factory factoryName] 
-        -data sampleData [-encoding charsetName] 
+Usage: opennlp LanguageDetectorTrainer[.leipzig] -model modelFile [-params paramsFile] [-factory factoryName]
+        -data sampleData [-encoding charsetName]
 Arguments description:
 	-model modelFile
 		output model file.
@@ -288,11 +325,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='3' valign='middle'>leipzig</entry>
@@ -335,11 +381,20 @@ Arguments description:
 Usage: opennlp LanguageDetectorConverter help|leipzig [help|options...]
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='3' valign='middle'>leipzig</entry>
@@ -379,9 +434,9 @@ Usage: opennlp LanguageDetectorConverter help|leipzig [help|options...]
 
 <screen>
 <![CDATA[
-Usage: opennlp LanguageDetectorCrossValidator[.leipzig] [-misclassified true|false] [-folds num] [-factory 
-        factoryName] [-params paramsFile] [-reportOutputFile outputFile] -data sampleData [-encoding 
-        charsetName] 
+Usage: opennlp LanguageDetectorCrossValidator[.leipzig] [-misclassified true|false] [-folds num] [-factory
+        factoryName] [-params paramsFile] [-reportOutputFile outputFile] -data sampleData [-encoding
+        charsetName]
 Arguments description:
 	-misclassified true|false
 		if true will print false negatives and false positives.
@@ -399,11 +454,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='3' valign='middle'>leipzig</entry>
@@ -443,8 +507,8 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp LanguageDetectorEvaluator[.leipzig] -model model [-misclassified true|false] 
-        [-reportOutputFile outputFile] -data sampleData [-encoding charsetName] 
+Usage: opennlp LanguageDetectorEvaluator[.leipzig] -model model [-misclassified true|false]
+        [-reportOutputFile outputFile] -data sampleData [-encoding charsetName]
 Arguments description:
 	-model model
 		the model file to be evaluated.
@@ -458,11 +522,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='3' valign='middle'>leipzig</entry>
@@ -519,7 +592,7 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 </section>
@@ -539,7 +612,7 @@ Arguments description:
 Usage: opennlp SimpleTokenizer < sentences
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.tokenizer.TokenizerME'>
@@ -553,7 +626,7 @@ Usage: opennlp SimpleTokenizer < sentences
 Usage: opennlp TokenizerME model < sentences
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.tokenizer.TokenizerTrainer'>
@@ -564,9 +637,9 @@ Usage: opennlp TokenizerME model < sentences
 
 <screen>
 <![CDATA[
-Usage: opennlp TokenizerTrainer[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.conllu] [-factory 
-        factoryName] [-abbDict path] [-alphaNumOpt isAlphaNumOpt] [-params paramsFile] -lang language -model 
-        modelFile -data sampleData [-encoding charsetName] 
+Usage: opennlp TokenizerTrainer[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.conllu] [-factory
+        factoryName] [-abbDict path] [-alphaNumOpt isAlphaNumOpt] [-params paramsFile] -lang language -model
+        modelFile -data sampleData [-encoding charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of TokenizerFactory where to get implementation and resources.
@@ -586,11 +659,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>irishsentencebank</entry>
@@ -738,8 +820,8 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp TokenizerMEEvaluator[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.conllu] -model 
-        model [-misclassified true|false] -data sampleData [-encoding charsetName] 
+Usage: opennlp TokenizerMEEvaluator[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.conllu] -model
+        model [-misclassified true|false] -data sampleData [-encoding charsetName]
 Arguments description:
 	-model model
 		the model file to be evaluated.
@@ -751,11 +833,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>irishsentencebank</entry>
@@ -903,9 +994,9 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp TokenizerCrossValidator[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.conllu] 
-        [-misclassified true|false] [-folds num] [-factory factoryName] [-abbDict path] [-alphaNumOpt 
-        isAlphaNumOpt] [-params paramsFile] -lang language -data sampleData [-encoding charsetName] 
+Usage: opennlp TokenizerCrossValidator[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.conllu]
+        [-misclassified true|false] [-folds num] [-factory factoryName] [-abbDict path] [-alphaNumOpt
+        isAlphaNumOpt] [-params paramsFile] -lang language -data sampleData [-encoding charsetName]
 Arguments description:
 	-misclassified true|false
 		if true will print false negatives and false positives.
@@ -927,11 +1018,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>irishsentencebank</entry>
@@ -1079,14 +1179,23 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp TokenizerConverter help|irishsentencebank|ad|pos|conllx|namefinder|parse|conllu 
-        [help|options...] 
+Usage: opennlp TokenizerConverter help|irishsentencebank|ad|pos|conllx|namefinder|parse|conllu
+        [help|options...]
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>irishsentencebank</entry>
@@ -1237,7 +1346,7 @@ Usage: opennlp TokenizerConverter help|irishsentencebank|ad|pos|conllx|namefinde
 Usage: opennlp DictionaryDetokenizer detokenizerDictionary
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 </section>
@@ -1257,7 +1366,7 @@ Usage: opennlp DictionaryDetokenizer detokenizerDictionary
 Usage: opennlp SentenceDetector model < sentences
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.sentdetect.SentenceDetectorTrainer'>
@@ -1268,10 +1377,10 @@ Usage: opennlp SentenceDetector model < sentences
 
 <screen>
 <![CDATA[
-Usage: opennlp 
-        SentenceDetectorTrainer[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.moses|.conllu|.letsmt] 
-        [-factory factoryName] [-eosChars string] [-abbDict path] [-params paramsFile] -lang language -model 
-        modelFile -data sampleData [-encoding charsetName] 
+Usage: opennlp
+        SentenceDetectorTrainer[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.moses|.conllu|.letsmt]
+        [-factory factoryName] [-eosChars string] [-abbDict path] [-params paramsFile] -lang language -model
+        modelFile -data sampleData [-encoding charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of SentenceDetectorFactory where to get implementation and resources.
@@ -1291,11 +1400,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>irishsentencebank</entry>
@@ -1475,9 +1593,9 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp 
-        SentenceDetectorEvaluator[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.moses|.conllu|.letsmt] 
-        -model model [-misclassified true|false] -data sampleData [-encoding charsetName] 
+Usage: opennlp
+        SentenceDetectorEvaluator[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.moses|.conllu|.letsmt]
+        -model model [-misclassified true|false] -data sampleData [-encoding charsetName]
 Arguments description:
 	-model model
 		the model file to be evaluated.
@@ -1489,11 +1607,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>irishsentencebank</entry>
@@ -1673,10 +1800,10 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp 
-        SentenceDetectorCrossValidator[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.moses|.conllu|.letsmt] 
-        [-factory factoryName] [-eosChars string] [-abbDict path] [-params paramsFile] -lang language 
-        [-misclassified true|false] [-folds num] -data sampleData [-encoding charsetName] 
+Usage: opennlp
+        SentenceDetectorCrossValidator[.irishsentencebank|.ad|.pos|.conllx|.namefinder|.parse|.moses|.conllu|.letsmt]
+        [-factory factoryName] [-eosChars string] [-abbDict path] [-params paramsFile] -lang language
+        [-misclassified true|false] [-folds num] -data sampleData [-encoding charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of SentenceDetectorFactory where to get implementation and resources.
@@ -1698,11 +1825,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>irishsentencebank</entry>
@@ -1882,14 +2018,23 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp SentenceDetectorConverter 
-        help|irishsentencebank|ad|pos|conllx|namefinder|parse|moses|conllu|letsmt [help|options...] 
+Usage: opennlp SentenceDetectorConverter
+        help|irishsentencebank|ad|pos|conllx|namefinder|parse|moses|conllu|letsmt [help|options...]
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='1' valign='middle'>irishsentencebank</entry>
@@ -2078,7 +2223,7 @@ Usage: opennlp SentenceDetectorConverter
 Usage: opennlp TokenNameFinder model1 model2 ... modelN < sentences
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.namefind.TokenNameFinderTrainer'>
@@ -2089,10 +2234,10 @@ Usage: opennlp TokenNameFinder model1 model2 ... modelN < sentences
 
 <screen>
 <![CDATA[
-Usage: opennlp TokenNameFinderTrainer[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat] 
-        [-factory factoryName] [-resources resourcesDir] [-type modelType] [-featuregen featuregenFile] 
-        [-nameTypes types] [-sequenceCodec codec] [-params paramsFile] -lang language -model modelFile -data 
-        sampleData [-encoding charsetName] 
+Usage: opennlp TokenNameFinderTrainer[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat]
+        [-factory factoryName] [-resources resourcesDir] [-type modelType] [-featuregen featuregenFile]
+        [-nameTypes types] [-sequenceCodec codec] [-params paramsFile] -lang language -model modelFile -data
+        sampleData [-encoding charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of TokenNameFinderFactory
@@ -2118,11 +2263,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='3' valign='middle'>evalita</entry>
@@ -2319,9 +2473,9 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp TokenNameFinderEvaluator[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat] 
-        [-nameTypes types] -model model [-misclassified true|false] [-detailedF true|false] 
-        [-reportOutputFile outputFile] -data sampleData [-encoding charsetName] 
+Usage: opennlp TokenNameFinderEvaluator[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat]
+        [-nameTypes types] -model model [-misclassified true|false] [-detailedF true|false]
+        [-reportOutputFile outputFile] -data sampleData [-encoding charsetName]
 Arguments description:
 	-nameTypes types
 		name types to use for evaluation
@@ -2339,11 +2493,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='3' valign='middle'>evalita</entry>
@@ -2540,12 +2703,12 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp 
-        TokenNameFinderCrossValidator[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat] 
-        [-factory factoryName] [-resources resourcesDir] [-type modelType] [-featuregen featuregenFile] 
-        [-nameTypes types] [-sequenceCodec codec] [-params paramsFile] -lang language [-misclassified 
-        true|false] [-folds num] [-detailedF true|false] [-reportOutputFile outputFile] -data sampleData 
-        [-encoding charsetName] 
+Usage: opennlp
+        TokenNameFinderCrossValidator[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat]
+        [-factory factoryName] [-resources resourcesDir] [-type modelType] [-featuregen featuregenFile]
+        [-nameTypes types] [-sequenceCodec codec] [-params paramsFile] -lang language [-misclassified
+        true|false] [-folds num] [-detailedF true|false] [-reportOutputFile outputFile] -data sampleData
+        [-encoding charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of TokenNameFinderFactory
@@ -2577,11 +2740,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='3' valign='middle'>evalita</entry>
@@ -2778,14 +2950,23 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp TokenNameFinderConverter help|evalita|ad|conll03|bionlp2004|conll02|muc6|ontonotes|brat 
-        [help|options...] 
+Usage: opennlp TokenNameFinderConverter help|evalita|ad|conll03|bionlp2004|conll02|muc6|ontonotes|brat
+        [help|options...]
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='3' valign='middle'>evalita</entry>
@@ -2991,7 +3172,7 @@ Arguments description:
 	-dict dict
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 </section>
@@ -3011,7 +3192,7 @@ Arguments description:
 Usage: opennlp POSTagger model < sentences
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.postag.POSTaggerTrainer'>
@@ -3022,9 +3203,9 @@ Usage: opennlp POSTagger model < sentences
 
 <screen>
 <![CDATA[
-Usage: opennlp POSTaggerTrainer[.ad|.conllx|.parse|.ontonotes|.conllu] [-factory factoryName] [-resources 
-        resourcesDir] [-tagDictCutoff tagDictCutoff] [-featuregen featuregenFile] [-dict dictionaryPath] 
-        [-params paramsFile] -lang language -model modelFile -data sampleData [-encoding charsetName] 
+Usage: opennlp POSTaggerTrainer[.ad|.conllx|.parse|.ontonotes|.conllu] [-factory factoryName] [-resources
+        resourcesDir] [-tagDictCutoff tagDictCutoff] [-featuregen featuregenFile] [-dict dictionaryPath]
+        [-params paramsFile] -lang language -model modelFile -data sampleData [-encoding charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of POSTaggerFactory where to get implementation and resources.
@@ -3048,11 +3229,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='4' valign='middle'>ad</entry>
@@ -3150,8 +3340,8 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp POSTaggerEvaluator[.ad|.conllx|.parse|.ontonotes|.conllu] -model model [-misclassified 
-        true|false] [-reportOutputFile outputFile] -data sampleData [-encoding charsetName] 
+Usage: opennlp POSTaggerEvaluator[.ad|.conllx|.parse|.ontonotes|.conllu] -model model [-misclassified
+        true|false] [-reportOutputFile outputFile] -data sampleData [-encoding charsetName]
 Arguments description:
 	-model model
 		the model file to be evaluated.
@@ -3165,11 +3355,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='4' valign='middle'>ad</entry>
@@ -3267,10 +3466,10 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp POSTaggerCrossValidator[.ad|.conllx|.parse|.ontonotes|.conllu] [-misclassified true|false] 
-        [-folds num] [-factory factoryName] [-resources resourcesDir] [-tagDictCutoff tagDictCutoff] 
-        [-featuregen featuregenFile] [-dict dictionaryPath] [-params paramsFile] -lang language 
-        [-reportOutputFile outputFile] -data sampleData [-encoding charsetName] 
+Usage: opennlp POSTaggerCrossValidator[.ad|.conllx|.parse|.ontonotes|.conllu] [-misclassified true|false]
+        [-folds num] [-factory factoryName] [-resources resourcesDir] [-tagDictCutoff tagDictCutoff]
+        [-featuregen featuregenFile] [-dict dictionaryPath] [-params paramsFile] -lang language
+        [-reportOutputFile outputFile] -data sampleData [-encoding charsetName]
 Arguments description:
 	-misclassified true|false
 		if true will print false negatives and false positives.
@@ -3298,11 +3497,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='4' valign='middle'>ad</entry>
@@ -3403,11 +3611,20 @@ Arguments description:
 Usage: opennlp POSTaggerConverter help|ad|conllx|parse|ontonotes|conllu [help|options...]
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='4' valign='middle'>ad</entry>
@@ -3514,7 +3731,7 @@ Usage: opennlp POSTaggerConverter help|ad|conllx|parse|ontonotes|conllu [help|op
 Usage: opennlp LemmatizerME model < sentences
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.lemmatizer.LemmatizerTrainerME'>
@@ -3525,8 +3742,8 @@ Usage: opennlp LemmatizerME model < sentences
 
 <screen>
 <![CDATA[
-Usage: opennlp LemmatizerTrainerME[.conllu] [-factory factoryName] [-params paramsFile] -lang language -model 
-        modelFile -data sampleData [-encoding charsetName] 
+Usage: opennlp LemmatizerTrainerME[.conllu] [-factory factoryName] [-params paramsFile] -lang language -model
+        modelFile -data sampleData [-encoding charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of LemmatizerFactory where to get implementation and resources.
@@ -3542,11 +3759,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='2' valign='middle'>conllu</entry>
@@ -3580,8 +3806,8 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp LemmatizerEvaluator[.conllu] -model model [-misclassified true|false] [-reportOutputFile 
-        outputFile] -data sampleData [-encoding charsetName] 
+Usage: opennlp LemmatizerEvaluator[.conllu] -model model [-misclassified true|false] [-reportOutputFile
+        outputFile] -data sampleData [-encoding charsetName]
 Arguments description:
 	-model model
 		the model file to be evaluated.
@@ -3595,11 +3821,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='2' valign='middle'>conllu</entry>
@@ -3642,7 +3877,7 @@ Arguments description:
 Usage: opennlp ChunkerME model < sentences
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.chunker.ChunkerTrainerME'>
@@ -3653,8 +3888,8 @@ Usage: opennlp ChunkerME model < sentences
 
 <screen>
 <![CDATA[
-Usage: opennlp ChunkerTrainerME[.ad] [-factory factoryName] [-params paramsFile] -lang language -model 
-        modelFile -data sampleData [-encoding charsetName] 
+Usage: opennlp ChunkerTrainerME[.ad] [-factory factoryName] [-params paramsFile] -lang language -model
+        modelFile -data sampleData [-encoding charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of ChunkerFactory where to get implementation and resources.
@@ -3670,11 +3905,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='4' valign='middle'>ad</entry>
@@ -3720,8 +3964,8 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp ChunkerEvaluator[.ad] -model model [-misclassified true|false] [-detailedF true|false] -data 
-        sampleData [-encoding charsetName] 
+Usage: opennlp ChunkerEvaluator[.ad] -model model [-misclassified true|false] [-detailedF true|false] -data
+        sampleData [-encoding charsetName]
 Arguments description:
 	-model model
 		the model file to be evaluated.
@@ -3735,11 +3979,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='4' valign='middle'>ad</entry>
@@ -3785,9 +4038,9 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp ChunkerCrossValidator[.ad] [-factory factoryName] [-params paramsFile] -lang language 
-        [-misclassified true|false] [-folds num] [-detailedF true|false] -data sampleData [-encoding 
-        charsetName] 
+Usage: opennlp ChunkerCrossValidator[.ad] [-factory factoryName] [-params paramsFile] -lang language
+        [-misclassified true|false] [-folds num] [-detailedF true|false] -data sampleData [-encoding
+        charsetName]
 Arguments description:
 	-factory factoryName
 		A sub-class of ChunkerFactory where to get implementation and resources.
@@ -3807,11 +4060,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='4' valign='middle'>ad</entry>
@@ -3860,11 +4122,20 @@ Arguments description:
 Usage: opennlp ChunkerConverter help|ad [help|options...]
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='4' valign='middle'>ad</entry>
@@ -3916,14 +4187,14 @@ Usage: opennlp ChunkerConverter help|ad [help|options...]
 
 <screen>
 <![CDATA[
-Usage: opennlp Parser [-bs n -ap n -k n -tk tok_model] model < sentences 
+Usage: opennlp Parser [-bs n -ap n -k n -tk tok_model] model < sentences
 -bs n: Use a beam size of n.
 -ap f: Advance outcomes in with at least f% of the probability mass.
 -k n: Show the top n parses.  This will also display their log-probabilities.
 -tk tok_model: Use the specified tokenizer model to tokenize the sentences. Defaults to a WhitespaceTokenizer.
 
 ]]>
-</screen> 
+</screen>
 </section>
 
 <section id='tools.cli.parser.ParserTrainer'>
@@ -3934,9 +4205,9 @@ Usage: opennlp Parser [-bs n -ap n -k n -tk tok_model] model < sentences
 
 <screen>
 <![CDATA[
-Usage: opennlp ParserTrainer[.ontonotes|.frenchtreebank] [-headRulesSerializerImpl className] -headRules 
-        headRulesFile [-parserType CHUNKING|TREEINSERT] [-fun true|false] [-params paramsFile] -lang language 
-        -model modelFile [-encoding charsetName] -data sampleData 
+Usage: opennlp ParserTrainer[.ontonotes|.frenchtreebank] [-headRulesSerializerImpl className] -headRules
+        headRulesFile [-parserType CHUNKING|TREEINSERT] [-fun true|false] [-params paramsFile] -lang language
+        -model modelFile [-encoding charsetName] -data sampleData
 Arguments description:
 	-headRulesSerializerImpl className
 		head rules artifact serializer class name
@@ -3958,11 +4229,20 @@ Arguments description:
 		data to be used, usually a file name.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='0' valign='middle'>ontonotes</entry>
@@ -3997,8 +4277,8 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp ParserEvaluator[.ontonotes|.frenchtreebank] -model model [-misclassified true|false] -data 
-        sampleData [-encoding charsetName] 
+Usage: opennlp ParserEvaluator[.ontonotes|.frenchtreebank] -model model [-misclassified true|false] -data
+        sampleData [-encoding charsetName]
 Arguments description:
 	-model model
 		the model file to be evaluated.
@@ -4010,11 +4290,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='0' valign='middle'>ontonotes</entry>
@@ -4052,11 +4341,20 @@ Arguments description:
 Usage: opennlp ParserConverter help|ontonotes|frenchtreebank [help|options...]
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='0' valign='middle'>ontonotes</entry>
@@ -4091,8 +4389,8 @@ Usage: opennlp ParserConverter help|ontonotes|frenchtreebank [help|options...]
 
 <screen>
 <![CDATA[
-Usage: opennlp BuildModelUpdater[.ontonotes|.frenchtreebank] -model modelFile [-params paramsFile] -lang 
-        language -data sampleData [-encoding charsetName] 
+Usage: opennlp BuildModelUpdater[.ontonotes|.frenchtreebank] -model modelFile [-params paramsFile] -lang
+        language -data sampleData [-encoding charsetName]
 Arguments description:
 	-model modelFile
 		output model file.
@@ -4106,11 +4404,20 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead>
+  <row>
+    <entry>Format</entry>
+    <entry>Argument</entry>
+    <entry>Value</entry>
+    <entry>Optional</entry>
+    <entry>Description</entry>
+  </row>
+</thead>
 <tbody>
 <row>
 <entry morerows='0' valign='middle'>ontonotes</entry>
@@ -4145,8 +4452,8 @@ Arguments description:
 
 <screen>
 <![CDATA[
-Usage: opennlp CheckModelUpdater[.ontonotes|.frenchtreebank] -model modelFile [-params paramsFile] -lang 
-        language -data sampleData [-encoding charsetName] 
+Usage: opennlp CheckModelUpdater[.ontonotes|.frenchtreebank] -model modelFile [-params paramsFile] -lang
+        language -data sampleData [-encoding charsetName]
 Arguments description:
 	-model modelFile
 		output model file.
@@ -4160,11 +4467,17 @@ Arguments description:
 		encoding for reading and writing text, if absent the system default is used.
 
 ]]>
-</screen> 
+</screen>
 <para>The supported formats and arguments are:</para>
 
-<informaltable frame='all'><tgroup cols='4' align='left' colsep='1' rowsep='1'>
-<thead><row><entry>Format</entry><entry>Argument</entry><entry>Value</entry><entry>Optional</entry><entry>Description</entry></row></thead>
+<informaltable frame='all'>
+<tgroup cols='5' align='left' colsep='1' rowsep='1'>
+<thead><row>
+  <entry>Format</entry>
+  <entry>Argument</entry>
+  <entry>Value</entry>
+  <entry>Optional</entry>
+  <entry>Description</entry></row></thead>
 <tbody>
 <row>
 <entry morerows='0' valign='middle'>ontonotes</entry>
diff --git a/opennlp-docs/src/docbkx/coref.xml b/opennlp-docs/src/docbkx/coref.xml
index d28ce9c90..279a42157 100644
--- a/opennlp-docs/src/docbkx/coref.xml
+++ b/opennlp-docs/src/docbkx/coref.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
diff --git a/opennlp-docs/src/docbkx/corpora.xml b/opennlp-docs/src/docbkx/corpora.xml
index d420ca746..c9992bf19 100644
--- a/opennlp-docs/src/docbkx/corpora.xml
+++ b/opennlp-docs/src/docbkx/corpora.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -25,8 +25,8 @@ under the License.
 
 	<title>Corpora</title>
 	<para>
-	    OpenNLP has built-in support to convert into the native training format or directly use
-        various corpora	needed by the different	trainable components.
+	OpenNLP has built-in support to convert into the native training format or directly use
+	various corpora	needed by the different	trainable components.
 	</para>
 	<section id="tools.corpora.conll">
 		<title>CONLL</title>
@@ -50,7 +50,10 @@ under the License.
 		(211727 tokens) and section 20 as test data (47377 tokens). The annotation of the 
 		data has been derived from the WSJ corpus by a program written by Sabine Buchholz 
 		from Tilburg University, The Netherlands. Both training and test data can be
-		obtained from <ulink url="https://www.clips.uantwerpen.be/conll2000/chunking/">https://www.clips.uantwerpen.be/conll2000/chunking/</ulink>.
+		obtained from 
+			<ulink url="https://www.clips.uantwerpen.be/conll2000/chunking/">
+				https://www.clips.uantwerpen.be/conll2000/chunking/
+			</ulink>.
 		</para>
 		</section>
 		<section id="tools.corpora.conll.2000.converting">
@@ -65,13 +68,11 @@ under the License.
 		<para>
 		 We can train the model for the Chunker using the train.txt available at CONLL 2000:
 		 <screen>
-			<![CDATA[
-$ opennlp ChunkerTrainerME -model en-chunker.bin -iterations 500 \
+<![CDATA[$ opennlp ChunkerTrainerME -model en-chunker.bin -iterations 500 \
                            -lang en -data train.txt -encoding UTF-8]]>
 		</screen>
 		<screen>
-			<![CDATA[
-Indexing events using cutoff of 5
+<![CDATA[Indexing events using cutoff of 5
 
 	Computing event counts...  done. 211727 events
 	Indexing...  done.
@@ -104,12 +105,10 @@ Wrote chunker model to path: .\en-chunker.bin]]>
 		<para>
 		We evaluate the model using the file test.txt  available at CONLL 2000:
 		<screen>
-			<![CDATA[
-$ opennlp ChunkerEvaluator -model en-chunker.bin -lang en -encoding utf8 -data test.txt]]>
+<![CDATA[$ opennlp ChunkerEvaluator -model en-chunker.bin -lang en -encoding utf8 -data test.txt]]>
 		</screen>
 		<screen>
-			<![CDATA[
-Loading Chunker model ... done (0,665s)
+<![CDATA[Loading Chunker model ... done (0,665s)
 current: 85,8 sent/s avg: 85,8 sent/s total: 86 sent
 current: 88,1 sent/s avg: 87,0 sent/s total: 174 sent
 current: 156,2 sent/s avg: 110,0 sent/s total: 330 sent
@@ -174,13 +173,11 @@ F-Measure: 0.9230575441395671]]>
 		I will use Spanish data as reference, but it would be the same operations to Dutch. You just must remember change “-lang es” to “-lang nl” and use
 		the correct training files. So to convert the information to the OpenNLP format: 
 		<screen>
-			<![CDATA[
-$ opennlp TokenNameFinderConverter conll02 -data esp.train -lang es -types per > es_corpus_train_persons.txt]]>
+<![CDATA[$ opennlp TokenNameFinderConverter conll02 -data esp.train -lang es -types per > es_corpus_train_persons.txt]]>
 		</screen>
 		Optionally, you can convert the training test samples as well.
 		<screen>
-			<![CDATA[
-$ opennlp TokenNameFinderConverter conll02 -data esp.testa -lang es -types per > corpus_testa.txt
+<![CDATA[$ opennlp TokenNameFinderConverter conll02 -data esp.testa -lang es -types per > corpus_testa.txt
 $ opennlp TokenNameFinderConverter conll02 -data esp.testb -lang es -types per > corpus_testb.txt]]>
 		</screen>
 		</para>
@@ -190,11 +187,9 @@ $ opennlp TokenNameFinderConverter conll02 -data esp.testb -lang es -types per >
 		<para>
 		To train the model for the name finder: 
 		<screen>
-			<![CDATA[
-\bin\opennlp TokenNameFinderTrainer -lang es -encoding u
+<![CDATA[\bin\opennlp TokenNameFinderTrainer -lang es -encoding u
 tf8 -iterations 500 -data es_corpus_train_persons.txt -model es_ner_person.bin
 
-
 Indexing events using cutoff of 5
 
         Computing event counts...  done. 264715 events
@@ -266,26 +261,27 @@ path: .\es_ner_person.bin]]>
 		The German data is a collection of articles from the German newspaper Frankfurter
 		Rundschau. The articles are part of the ECI Multilingual Text Corpus which
 		can be obtained for 75$ (2010) from the Linguistic Data Consortium:
-<ulink url="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC94T5">http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC94T5</ulink>		</para>
+			<ulink url="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC94T5">
+				http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC94T5
+			</ulink>
+		</para>
 		<para>After one of the corpora is available the data must be
 		transformed as explained in the README file to the CONLL format.
 		The transformed data can be read by the OpenNLP CONLL03 converter.
-
-      Note that for CoNLL-2003 corpora, the -lang argument should either be "eng" or "deu", instead of "en" or "de".
+		Note that for CoNLL-2003 corpora, the -lang argument should either be "eng" or "deu", instead of "en" or "de".
 		</para>
 		</section>
+			
 		<section id="tools.corpora.conll.2003.converting">
 		<title>Converting the data (optional)</title>
 		<para>
 		To convert the information to the OpenNLP format:
 		<screen>
-			<![CDATA[
-$ opennlp TokenNameFinderConverter conll03 -lang eng -types per -data corpus_train.txt > eng.train]]>
+<![CDATA[$ opennlp TokenNameFinderConverter conll03 -lang eng -types per -data corpus_train.txt > eng.train]]>
 		</screen>
 		Optionally, you can convert the training test samples as well.
 		<screen>
-			<![CDATA[
-$ opennlp TokenNameFinderConverter conll03 -lang eng -types per -data corpus_testa.txt > eng.testa
+<![CDATA[$ opennlp TokenNameFinderConverter conll03 -lang eng -types per -data corpus_testa.txt > eng.testa
 $ opennlp TokenNameFinderConverter conll03 -lang eng -types per -data corpus_testb.txt > eng.testb]]>
 		</screen>
 		</para>
@@ -295,24 +291,21 @@ $ opennlp TokenNameFinderConverter conll03 -lang eng -types per -data corpus_tes
             <para>
                 You can train the model for the name finder this way:
                 <screen>
-                <![CDATA[
-$ opennlp TokenNameFinderTrainer.conll03 -model en_ner_person.bin \
+<![CDATA[$ opennlp TokenNameFinderTrainer.conll03 -model en_ner_person.bin \
                                  -lang eng -types per -data eng.train -encoding utf8]]>
                 </screen>
             </para>
 		    <para>
                 If you have converted the data, then you can train the model for the name finder this way:
                 <screen>
-                <![CDATA[
-$ opennlp TokenNameFinderTrainer.conll03 -model en_ner_person.bin \
+<![CDATA[$ opennlp TokenNameFinderTrainer.conll03 -model en_ner_person.bin \
                                  -lang eng -types per -data corpus_train.txt -encoding utf8]]>
 		        </screen>
             </para>
             <para>
                 Either way you should see the following output during the training process:
 		        <screen>
-			    <![CDATA[
-Indexing events with TwoPass using cutoff of 0
+<![CDATA[Indexing events with TwoPass using cutoff of 0
 
 	Computing event counts...  done. 203621 events
 	Indexing...  done.
@@ -361,28 +354,23 @@ Execution time: 11,498 seconds]]>
             <para>
                 You can evaluate the model for the name finder this way:
                 <screen>
-                <![CDATA[
-$ opennlp TokenNameFinderEvaluator.conll03 -model en_ner_person.bin \
+<![CDATA[$ opennlp TokenNameFinderEvaluator.conll03 -model en_ner_person.bin \
                                    -lang eng -types per -data eng.testa -encoding utf8]]>
                 </screen>
             </para>
 		    <para>
-		        If you converted the test A and B files above, you can use them to evaluate the
-                model.
+		        If you converted the test A and B files above, you can use them to evaluate the model.
 		        <screen>
-			<![CDATA[
-$ opennlp TokenNameFinderEvaluator.conll03 -model en_ner_person.bin \
+<![CDATA[$ opennlp TokenNameFinderEvaluator.conll03 -model en_ner_person.bin \
                                    -lang eng -types per -data corpus_testa.txt -encoding utf8]]>
 		        </screen>
             </para>
             <para>
                 Either way you should see the following output:
 		        <screen>
-			<![CDATA[
-Loading Token Name Finder model ... done (0,176s)
+<![CDATA[Loading Token Name Finder model ... done (0,176s)
 current: 1805,4 sent/s avg: 1805,4 sent/s total: 1961 sent
 
-
 Average: 2298,1 sent/s
 Total: 3454 sent
 Runtime: 1.503s
@@ -418,15 +406,13 @@ Execution time: 1,955 seconds]]>
 			    <para>
 				    To extract NameFinder training data from Amazonia corpus:
 			        <screen>
-			        <![CDATA[
-$ opennlp TokenNameFinderConverter ad -lang pt -encoding ISO-8859-1 -data amazonia.ad > corpus.txt]]>
+<![CDATA[$ opennlp TokenNameFinderConverter ad -lang pt -encoding ISO-8859-1 -data amazonia.ad > corpus.txt]]>
 			        </screen>
 			</para>
 			<para>
 				To extract Chunker training data from Bosque_CF_8.0.ad corpus:
 			    <screen>
-			    <![CDATA[
-$ opennlp ChunkerConverter ad -lang pt -data Bosque_CF_8.0.ad.txt -encoding ISO-8859-1 > bosque-chunk]]>
+<![CDATA[$ opennlp ChunkerConverter ad -lang pt -data Bosque_CF_8.0.ad.txt -encoding ISO-8859-1 > bosque-chunk]]>
     			</screen>
 			</para>
 		</section>
@@ -435,13 +421,11 @@ $ opennlp ChunkerConverter ad -lang pt -data Bosque_CF_8.0.ad.txt -encoding ISO-
 			    <para>
 			        To perform the evaluation the corpus was split into a training and a test part.
 			        <screen>
-			        <![CDATA[
-$ sed '1,55172d' corpus.txt > corpus_train.txt
+<![CDATA[$ sed '1,55172d' corpus.txt > corpus_train.txt
 $ sed '55172,100000000d' corpus.txt > corpus_test.txt]]>
         			</screen>
         			<screen>
-        			<![CDATA[
-$ opennlp TokenNameFinderTrainer -model pt-ner.bin -cutoff 20 -lang PT -data corpus_train.txt -encoding UTF-8
+<![CDATA[$ opennlp TokenNameFinderTrainer -model pt-ner.bin -cutoff 20 -lang PT -data corpus_train.txt -encoding UTF-8
 ...
 $ opennlp TokenNameFinderEvaluator -model pt-ner.bin -lang PT -data corpus_train.txt -encoding UTF-8
 
@@ -502,8 +486,7 @@ F-Measure: 0.7717879983140168]]>
 		<para>
 		The sample shows how to train a model to detect person names.		
 			<programlisting>
-			<![CDATA[
-$ bin/opennlp TokenNameFinderTrainer.ontonotes -lang en -model en-ontonotes.bin \
+<![CDATA[$ bin/opennlp TokenNameFinderTrainer.ontonotes -lang en -model en-ontonotes.bin \
 			 -nameTypes person -ontoNotesDir ontonotes-release-4.0/data/files/data/english/
 			 
 Indexing events using cutoff of 5
@@ -546,7 +529,7 @@ path: /dev/opennlp/trunk/opennlp-tools/en-ontonotes.bin]]>
 
 		<mediaobject>
 			<imageobject>
-				<imagedata fileref="images/brat.png" width="6.5in" depth="4in" scalefit="1"/>
+				<imagedata fileref="images/brat.png" width="5.5in" scalefit="1"/>
 			</imageobject>
 		</mediaobject>
 
@@ -573,47 +556,45 @@ path: /dev/opennlp/trunk/opennlp-tools/en-ontonotes.bin]]>
 				a rule based tokenizer.
 
 				<screen>
-					<![CDATA[
-$ opennlp TokenNameFinderTrainer.brat
+<![CDATA[$ opennlp TokenNameFinderTrainer.brat
 Usage: opennlp TokenNameFinderTrainer.brat [-factory factoryName] [-resources resourcesDir] [-type modelType]
 [-featuregen featuregenFile] [-nameTypes types] [-sequenceCodec codec] [-params paramsFile] -lang language
 -model modelFile [-tokenizerModel modelFile] [-ruleBasedTokenizer name] -annotationConfig annConfFile
 -bratDataDir bratDataDir [-recursive value] [-sentenceDetectorModel modelFile]
 
 Arguments description:
-	-factory factoryName
-		A sub-class of TokenNameFinderFactory
-	-resources resourcesDir
-		The resources directory
-	-type modelType
-		The type of the token name finder model
-	-featuregen featuregenFile
-		The feature generator descriptor file
-	-nameTypes types
-		name types to use for training
-	-sequenceCodec codec
-		sequence codec used to code name spans
-	-params paramsFile
-		training parameters file.
-	-lang language
-		language which is being processed.
-	-model modelFile
-		output model file.
-	-tokenizerModel modelFile
-	-ruleBasedTokenizer name
-	-annotationConfig annConfFile
-	-bratDataDir bratDataDir
-		location of brat data dir
-	-recursive value
-	-sentenceDetectorModel modelFile
+		-factory factoryName
+			A sub-class of TokenNameFinderFactory
+		-resources resourcesDir
+			The resources directory
+		-type modelType
+			The type of the token name finder model
+		-featuregen featuregenFile
+			The feature generator descriptor file
+		-nameTypes types
+			name types to use for training
+		-sequenceCodec codec
+			sequence codec used to code name spans
+		-params paramsFile
+			training parameters file.
+		-lang language
+			language which is being processed.
+		-model modelFile
+			output model file.
+		-tokenizerModel modelFile
+		-ruleBasedTokenizer name
+		-annotationConfig annConfFile
+		-bratDataDir bratDataDir
+			location of brat data dir
+		-recursive value
+		-sentenceDetectorModel modelFile
 ]]>
 				</screen>
 
 				The following command will train a danish organization name finder model.
 
 				<screen>
-					<![CDATA[
-$ opennlp TokenNameFinderTrainer.brat -resources conf/resources \
+<![CDATA[$ opennlp TokenNameFinderTrainer.brat -resources conf/resources \
 -featuregen conf/resources/fg-da-org.xml -nameTypes Organization \
 -params conf/resources/TrainerParams.txt -lang da \
 -model models/da-org.bin -ruleBasedTokenizer simple \
@@ -660,8 +641,7 @@ Compressed 1403655 parameters to 116378
 done (11.099s)
 
 Wrote name finder model to
-path: models/da-org.bin
-]]>
+path: models/da-org.bin]]>
 				</screen>
 			</para>
 		</section>
@@ -678,8 +658,7 @@ path: models/da-org.bin
 				it should be able to identify. Shuffling the data before splitting is most likely sufficient in many cases.
 
 				<screen>
-					<![CDATA[
-$ opennlp TokenNameFinderEvaluator.brat -model models/da-org.bin \
+<![CDATA[$ opennlp TokenNameFinderEvaluator.brat -model models/da-org.bin \
 -ruleBasedTokenizer simple -annotationConfig data/annotation.conf \
 -bratDataDir data/gold/da/test -recursive true \
 -sentenceDetectorModel models/da-sent.bin
@@ -692,9 +671,7 @@ Runtime: 10.043s
 
 Precision: 0.7321974661424203
 Recall: 0.25176505933603727
-F-Measure: 0.3746926000447127
-
-]]>
+F-Measure: 0.3746926000447127]]>
 				</screen>
 			</para>
 		</section>
@@ -707,8 +684,7 @@ F-Measure: 0.3746926000447127
 				Running cross validation with the misclassified attribute set to true can also be helpful because it
 				will identify missed annotations as they will pop up as false positives in the text output.
 				<screen>
-					<![CDATA[
-$ opennlp TokenNameFinderCrossValidator.brat -resources conf/resources \
+<![CDATA[$ opennlp TokenNameFinderCrossValidator.brat -resources conf/resources \
 -featuregen conf/resources/fg-da-org.xml -nameTypes Organization \
 -params conf/resources/TrainerParams.txt -lang da -misclassified true \
 -folds 10 -detailedF true -ruleBasedTokenizer simple -annotationConfig data/annotation.conf \
@@ -737,10 +713,7 @@ done
 
 Evaluated 26133 samples with 13053 entities; found: 12174 entities; correct: 10361.
        TOTAL: precision:   85.11%;  recall:   79.38%; F1:   82.14%.
-Organization: precision:   85.11%;  recall:   79.38%; F1:   82.14%. [target: 13053; tp: 10361; fp: 1813]
-
-
-        ]]>
+Organization: precision:   85.11%;  recall:   79.38%; F1:   82.14%. [target: 13053; tp: 10361; fp: 1813]]]>
 				</screen>
 			</para>
 		</section>
diff --git a/opennlp-docs/src/docbkx/css/opennlp-docs.css b/opennlp-docs/src/docbkx/css/opennlp-dev-manual.css
similarity index 74%
rename from opennlp-docs/src/docbkx/css/opennlp-docs.css
rename to opennlp-docs/src/docbkx/css/opennlp-dev-manual.css
index 172ee44dc..920d64042 100644
--- a/opennlp-docs/src/docbkx/css/opennlp-docs.css
+++ b/opennlp-docs/src/docbkx/css/opennlp-dev-manual.css
@@ -40,7 +40,20 @@ div.book {
     margin: 0 auto;
     font-family: var(--font-family);
     padding: 1rem;
-    background-color: white;
+}
+
+div.cover {
+    text-align: center;
+    page-break-after: always;
+    padding: 0;
+    margin: 0;
+}
+
+div.cover img {
+    height: 100%;
+    max-width: 100%;
+    padding: 10px;
+    margin: 0;
 }
 
 div.toc > dl, div.list-of-tables > dl {
@@ -53,12 +66,53 @@ a {
     text-decoration: none;
 }
 
-h1, h2, h3, h4, div.toc {
+/* font plan- serif text, sans headers */
+h1, h2, h3, h4, h5, h6, div.toc {
     color: var(--red-color);
+    hyphens: none !important;
+    -moz-hyphens: none !important;
+    -webkit-hyphens: none !important;
+    adobe-hyphenate: none !important;
+    page-break-after: avoid;
+    page-break-inside: avoid;
+    text-indent: 0;
+    text-align: left;
+}
+
+h1 + p, h2 + p, h3 + p, h4 + p, h5 + p, h6 + p {
+    text-indent: 0;
+}
+
+p {
+    font-family: var(--font-family);
+    -webkit-hyphens: auto;
+    -moz-hyphens: auto;
+    hyphens: auto;
+    hyphenate-after: 3;
+    hyphenate-before: 3;
+    hyphenate-lines: 2;
+    -webkit-hyphenate-after: 3;
+    -webkit-hyphenate-before: 3;
+    -webkit-hyphenate-lines: 2;
+    line-height: 1.5em;
+    margin: 0;
+    text-align: justify;
+    orphans: 2;
+    widows: 2;
+}
+p.first-para, p.first-para-chapter, p.note-p-first {
+    text-indent: 0;
+}
+p.first-para-chapter::first-line {
+    /* handle run-in */
+    font-variant: small-caps;
+}
+p.no-indent {
+    text-indent: 0;
 }
 
 div.legalnotice {
-    max-width: 450px;
+    max-width: 660px;
 }
 
 pre {
@@ -74,7 +128,9 @@ pre.programlisting, pre.screen, pre.literallayout {
     background-color: #EEE;
     font-family: var(--code-font-family);
     white-space: pre;
-    padding: 0 1rem;
+    padding-top: 1rem;
+    padding-left: 1rem;
+    padding-bottom: 0;
 }
 
 /*
diff --git a/opennlp-docs/src/docbkx/doccat.xml b/opennlp-docs/src/docbkx/doccat.xml
index 5c486dd62..66ac31f77 100644
--- a/opennlp-docs/src/docbkx/doccat.xml
+++ b/opennlp-docs/src/docbkx/doccat.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -30,15 +30,13 @@ under the License.
 		It is based on maximum entropy framework. For someone interested in Gross Margin,
 		the sample text given below could be classified as GMDecrease
         <screen>
-			<![CDATA[
-Major acquisitions that have a lower gross margin than the existing network
+<![CDATA[Major acquisitions that have a lower gross margin than the existing network
 also had a negative impact on the overall gross margin, but it should improve
 following the implementation of its integration strategies.]]>
 		 </screen>
 and the text below could be classified as GMIncrease
         <screen>
-			<![CDATA[
-The upward movement of gross margin resulted from amounts pursuant to 
+<![CDATA[The upward movement of gross margin resulted from amounts pursuant to
 adjustments to obligations towards dealers.]]>
 		 </screen>
 		 To be able to classify a text, the document categorizer needs a model. 
@@ -57,8 +55,7 @@ adjustments to obligations towards dealers.]]>
 		The easiest way to try out the document categorizer is the command line tool. The tool is only
 		intended for demonstration and testing. The following command shows how to use the document categorizer tool.
 		  <screen>
-			<![CDATA[
-$ opennlp Doccat model]]>
+<![CDATA[$ opennlp Doccat model]]>
 		 </screen>
 		 The input is read from standard input and output is written to standard output, unless they are redirected
 		 or piped. As with most components in OpenNLP, document categorizer expects input which is segmented into sentences.
@@ -74,29 +71,26 @@ $ opennlp Doccat model]]>
 		<para>
 			Using an OpenNLP model, first you need to grab the bytes from the serialized model on an InputStream:
 						<programlisting language="java">
-				<![CDATA[
-InputStream is = ...
+<![CDATA[InputStream is = ...
 DoccatModel m = new DoccatModel(is);]]>
 				</programlisting>
 				With the DoccatModel in hand we are just about there:
 						<programlisting language="java">
-				<![CDATA[
-String inputText = ...
+<![CDATA[String inputText = ...
 String[] textTokens = inputText.split(" "); // split by whitespace
 DocumentCategorizerME myCategorizer = new DocumentCategorizerME(m);
 double[] outcomes = myCategorizer.categorize(textTokens);
 String category = myCategorizer.getBestCategory(outcomes);]]>
 				</programlisting>
 		</para>
-		<section id="tools.namefind.api.onnx">
+		<section id="tools.doccat.api.onnx">
 			<title>Using an ONNX Model</title>
 			<para>
 				Using an ONNX model is similar, except we will utilize the <code>DocumentCategorizerDL</code> class instead.
 				You must provide the path to the model file and the vocabulary file to the document categorizer.
 				(There is no need to load the model as an InputStream as in the previous example.)
 				<programlisting language="java">
-					<![CDATA[
-File model = new File("/path/to/model.onnx");
+<![CDATA[File model = new File("/path/to/model.onnx");
 File vocab = new File("/path/to/vocab.txt");
 Map<Integer, String> categories = new HashMap<>();
 String[] inputText = new String[]{"My input text is great."};
@@ -119,12 +113,11 @@ String category = myCategorizer.getBestCategory(outcomes);]]>
 			The following sample shows the sample from above in the required format. Here GMDecrease and GMIncrease
 			are the categories.
 			<screen>
-			<![CDATA[
-GMDecrease Major acquisitions that have a lower gross margin than the existing network also \ 
-           had a negative impact on the overall gross margin, but it should improve following \ 
-           the implementation of its integration strategies .
+<![CDATA[MDecrease Major acquisitions that have a lower gross margin than the existing network also \
+	 had a negative impact on the overall gross margin, but it should improve following \
+	 the implementation of its integration strategies .
 GMIncrease The upward movement of gross margin resulted from amounts pursuant to adjustments \
-           to obligations towards dealers .]]>
+	 to obligations towards dealers .]]>
 			</screen>
 			<emphasis role="strong">Note</emphasis>: The line breaks marked with a backslash are just inserted for formatting purposes and must not be
 			included in the training data.
@@ -132,15 +125,14 @@ GMIncrease The upward movement of gross margin resulted from amounts pursuant to
 		<section id="tools.doccat.training.tool">
 		<title>Training Tool</title>
 		<para>
-		The following command will train the document categorizer and write the model to en-doccat.bin:		
+		The following command will train the document categorizer and write the model to en-doccat.bin:
 		  <screen>
-			<![CDATA[			
-$ opennlp DoccatTrainer -model en-doccat.bin -lang en -data en-doccat.train -encoding UTF-8]]>
+<![CDATA[$ opennlp DoccatTrainer -model en-doccat.bin -lang en -data en-doccat.train -encoding UTF-8]]>
 		 </screen>
 		Additionally it is possible to specify the number of iterations, and the cutoff.
 		</para>
 		</section>
-				<section id="tools.doccat.training.api">
+		<section id="tools.doccat.training.api">
 		<title>Training API</title>
 		<para>
 		So, naturally you will need some access to many pre-classified events to train your model.
@@ -150,8 +142,7 @@ $ opennlp DoccatTrainer -model en-doccat.bin -lang en -data en-doccat.train -enc
 		your training data was contained in a text file, where the format is as described above.
 		Then you might want to write something like this to create a collection of DocumentSamples:
 		<programlisting language="java">
-						<![CDATA[
-DoccatModel model = null;
+<![CDATA[DoccatModel model = null;
 try {
   ObjectStream<String> lineStream =
 		new PlainTextByLineStream(new MarkableFileInputStreamFactory(new File("en-sentiment.train")), StandardCharsets.UTF_8);
@@ -162,8 +153,7 @@ try {
       TrainingParameters.defaultParams(), new DoccatFactory());
 } catch (IOException e) {
   e.printStackTrace();
-}
-]]>
+}]]>
 	</programlisting>
 	Now might be a good time to cruise over to Hulu or something, because this could take a while if you've got a large training set.
 	You may see a lot of output as well. Once you're done, you can pretty quickly step to classification directly,
@@ -171,11 +161,9 @@ try {
 		</para>
 		<para>
 		<programlisting language="java">
-						<![CDATA[
-try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(modelFile))) {
+<![CDATA[try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(modelFile))) {
   model.serialize(modelOut);
-}
-]]>
+}]]>
 </programlisting>
 		</para>
 		</section>
diff --git a/opennlp-docs/src/docbkx/evaltest.xml b/opennlp-docs/src/docbkx/evaltest.xml
index 620a136fa..8c8361513 100644
--- a/opennlp-docs/src/docbkx/evaltest.xml
+++ b/opennlp-docs/src/docbkx/evaltest.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
diff --git a/opennlp-docs/src/docbkx/extension.xml b/opennlp-docs/src/docbkx/extension.xml
index afd5b032c..11be740fb 100644
--- a/opennlp-docs/src/docbkx/extension.xml
+++ b/opennlp-docs/src/docbkx/extension.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
diff --git a/opennlp-docs/src/docbkx/introduction.xml b/opennlp-docs/src/docbkx/introduction.xml
index 1aee630a7..b8eef6f56 100644
--- a/opennlp-docs/src/docbkx/introduction.xml
+++ b/opennlp-docs/src/docbkx/introduction.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -55,35 +55,35 @@ under the License.
     </section>
 
     <section id="intro.api">
-        <title>Application Program Interface (API). Generic Example</title>
+        <title>Application Program Interface (API)</title>
         <para>
             OpenNLP components have similar APIs. Normally, to execute a task,
             one should provide a model and an input.
         </para>
-        <para>
-            A model is usually loaded by providing a FileInputStream with a model to a
-            constructor of the model class:
-            <programlisting language="java">
-                    <![CDATA[
-try (InputStream modelIn = new FileInputStream("lang-model-name.bin")) {
+        <section id="intro.api.example">
+            <title>Generic Example</title>
+            <para>
+                A model is usually loaded by providing a FileInputStream with a model to a
+                constructor of the model class:
+                <programlisting language="java">
+<![CDATA[try (InputStream modelIn = new FileInputStream("lang-model-name.bin")) {
   SomeModel model = new SomeModel(modelIn);
 }]]>
+                </programlisting>
+            </para>
+            <para>
+            After the model is loaded the tool itself can be instantiated.
+            <programlisting language="java">
+<![CDATA[ToolName toolName = new ToolName(model);]]>
             </programlisting>
-        </para>
-        <para>
-        After the model is loaded the tool itself can be instantiated.
-        <programlisting language="java">
-                <![CDATA[
-ToolName toolName = new ToolName(model);]]>
-        </programlisting>
-        After the tool is instantiated, the processing task can be executed. The input and the
-        output formats are specific to the tool, but often the output is an array of String,
-        and the input is a String or an array of String.
-        <programlisting language="java">
-                <![CDATA[
-String[] output = toolName.executeTask("This is a sample text.");]]>
-        </programlisting>
-        </para>
+            After the tool is instantiated, the processing task can be executed. The input and the
+            output formats are specific to the tool, but often the output is an array of String,
+            and the input is a String or an array of String.
+            <programlisting language="java">
+<![CDATA[String[] output = toolName.executeTask("This is a sample text.");]]>
+            </programlisting>
+            </para>
+        </section>
     </section>
 
     <section id="intro.cli">
@@ -130,13 +130,11 @@ String[] output = toolName.executeTask("This is a sample text.");]]>
             <para>
                 Apache OpenNLP provides a common command line script to access all its tools:
                 <screen>
-                <![CDATA[
-$ opennlp]]>
+<![CDATA[$ opennlp]]>
                  </screen>
                 This script prints current version of the library and lists all available tools:
                 <screen>
-                <![CDATA[
-OpenNLP <VERSION>. Usage: opennlp TOOL
+<![CDATA[OpenNLP <VERSION>. Usage: opennlp TOOL
 where TOOL is one of:
   Doccat                            learnable document categorizer
   DoccatTrainer                     trainer for the learnable document categorizer
@@ -183,16 +181,14 @@ Example: opennlp SimpleTokenizer help]]>
             <para>OpenNLP tools have similar command line structure and options. To discover tool
                 options, run it with no parameters:
                 <screen>
-                <![CDATA[
-$ opennlp ToolName]]>
+<![CDATA[$ opennlp ToolName]]>
                  </screen>
                 The tool will output two blocks of help.
             </para>
             <para>
                 The first block describes the general structure of this tool command line:
                 <screen>
-                <![CDATA[
-Usage: opennlp TokenizerTrainer[.namefinder|.conllx|.pos] [-abbDict path] ...  -model modelFile ...]]>
+<![CDATA[Usage: opennlp TokenizerTrainer[.namefinder|.conllx|.pos] [-abbDict path] ...  -model modelFile ...]]>
                 </screen>
                 The general structure of this tool command line includes the obligatory tool name
                 (TokenizerTrainer), the optional format parameters ([.namefinder|.conllx|.pos]),
@@ -204,12 +200,10 @@ Usage: opennlp TokenizerTrainer[.namefinder|.conllx|.pos] [-abbDict path] ...  -
                 Each format might have its own parameters, which are displayed if the tool is
                 executed without or with help parameter:
                 <screen>
-                <![CDATA[
-$ opennlp TokenizerTrainer.conllx help]]>
+<![CDATA[$ opennlp TokenizerTrainer.conllx help]]>
                 </screen>
                 <screen>
-                <![CDATA[
-Usage: opennlp TokenizerTrainer.conllx [-abbDict path] [-alphaNumOpt isAlphaNumOpt] ...
+<![CDATA[Usage: opennlp TokenizerTrainer.conllx [-abbDict path] [-alphaNumOpt isAlphaNumOpt] ...
 
 Arguments description:
         -abbDict path
@@ -219,15 +213,13 @@ Arguments description:
                 To switch the tool to a specific format, add a dot and the format name after
                 the tool name:
                 <screen>
-                <![CDATA[
-$ opennlp TokenizerTrainer.conllx -model en-pos.bin ...]]>
+<![CDATA[$ opennlp TokenizerTrainer.conllx -model en-pos.bin ...]]>
                 </screen>
             </para>
             <para>
                 The second block of the help message describes the individual arguments:
                 <screen>
-                <![CDATA[
-Arguments description:
+<![CDATA[Arguments description:
         -type maxent|perceptron|perceptron_sequence
                 The type of the token name finder model. One of maxent|perceptron|perceptron_sequence.
         -dict dictionaryPath
@@ -238,8 +230,7 @@ Arguments description:
             <para>
                 Most tools for processing need to be provided at least a model:
                 <screen>
-                <![CDATA[
-$ opennlp ToolName lang-model-name.bin]]>
+<![CDATA[$ opennlp ToolName lang-model-name.bin]]>
                  </screen>
                 When tool is executed this way, the model is loaded and the tool is waiting for
                 the input from standard input. This input is processed and printed to standard
@@ -248,8 +239,7 @@ $ opennlp ToolName lang-model-name.bin]]>
             <para>Alternative, or one should say, most commonly used way is to use console input and
                 output redirection options to provide also an input and an output files:
                 <screen>
-            <![CDATA[
-$ opennlp ToolName lang-model-name.bin < input.txt > output.txt]]>
+<![CDATA[$ opennlp ToolName lang-model-name.bin < input.txt > output.txt]]>
                 </screen>
             </para>
             <para>
@@ -273,13 +263,11 @@ $ opennlp ToolName lang-model-name.bin < input.txt > output.txt]]>
             <para>
                 A generic example of a command line to launch a tool trainer might be:
                 <screen>
-                <![CDATA[
-$ opennlp ToolNameTrainer -model en-model-name.bin -lang en -data input.train -encoding UTF-8]]>
+<![CDATA[$ opennlp ToolNameTrainer -model en-model-name.bin -lang en -data input.train -encoding UTF-8]]>
                  </screen>
                 or with a format:
                 <screen>
-                <![CDATA[
-$ opennlp ToolNameTrainer.conll03 -model en-model-name.bin -lang en -data input.train \
+<![CDATA[$ opennlp ToolNameTrainer.conll03 -model en-model-name.bin -lang en -data input.train \
                                   -types per -encoding UTF-8]]>
                  </screen>
             </para>
@@ -288,8 +276,7 @@ $ opennlp ToolNameTrainer.conll03 -model en-model-name.bin -lang en -data input.
                 as whether to print misclassified samples), and then the test data. A generic
                 example of a command line to launch an evaluation tool might be:
                 <screen>
-                <![CDATA[
-$ opennlp ToolNameEvaluator -model en-model-name.bin -lang en -data input.test -encoding UTF-8]]>
+<![CDATA[$ opennlp ToolNameEvaluator -model en-model-name.bin -lang en -data input.test -encoding UTF-8]]>
                  </screen>
             </para>
         </section>
@@ -310,7 +297,7 @@ $ opennlp ToolNameEvaluator -model en-model-name.bin -lang en -data input.test -
         <section id="intro.models.onnx">
             <title>ONNX Models</title>
             <para>
-                OpenNLP supports ONNX models via the ONNX Runtime for the <link linkend="tools.namefind">Name Finder</link>.
+                OpenNLP supports ONNX models via the ONNX Runtime for the <link linkend="tools.namefind">Name Finder</link>
                 and <link linkend="tools.doccat">Document Categorizer</link>. This allows models trained by other frameworks
                 such as PyTorch and Tensorflow to be used by OpenNLP. The documentation for
                 each of the OpenNLP components that supports ONNX models describes how to
@@ -333,8 +320,7 @@ $ opennlp ToolNameEvaluator -model en-model-name.bin -lang en -data input.test -
             <para>
                 Users may override by setting the following system property:
                 <screen>
-                    <![CDATA[
--Dopennlp.interner.class=opennlp.tools.util.jvm.JvmStringInterner]]>
+<![CDATA[-Dopennlp.interner.class=opennlp.tools.util.jvm.JvmStringInterner]]>
                 </screen>
             </para>
             <para>
diff --git a/opennlp-docs/src/docbkx/langdetect.xml b/opennlp-docs/src/docbkx/langdetect.xml
index 0e510c596..2082c4ebe 100644
--- a/opennlp-docs/src/docbkx/langdetect.xml
+++ b/opennlp-docs/src/docbkx/langdetect.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -79,8 +79,7 @@ under the License.
 		The easiest way to try out the language detector is the command line tool. The tool is only
 		intended for demonstration and testing. The following command shows how to use the language detector tool.
 		  <screen>
-			<![CDATA[
-$ bin/opennlp LanguageDetector model]]>
+<![CDATA[$ bin/opennlp LanguageDetector model]]>
 		 </screen>
 		 The input is read from standard input and output is written to standard output, unless they are redirected
 		 or piped.
@@ -96,14 +95,12 @@ $ bin/opennlp LanguageDetector model]]>
 			First you need to grab the bytes from the serialized model on an InputStream - 
 			we'll leave it you to do that, since you were the one who serialized it to begin with. Now for the easy part:
 						<programlisting language="java">
-				<![CDATA[
-InputStream is = ...
+<![CDATA[InputStream is = ...
 LanguageDetectorModel m = new LanguageDetectorModel(is);]]>
 				</programlisting>
 				With the LanguageDetectorModel in hand we are just about there:
 						<programlisting language="java">
-				<![CDATA[
-String inputText = ...
+<![CDATA[String inputText = ...
 LanguageDetector myCategorizer = new LanguageDetectorME(m);
 
 // Get the most probable language
@@ -128,8 +125,7 @@ Language[] languages = myCategorizer.predictLanguages(null);]]>
 			available.
 			The following sample shows the sample from above in the required format.
 			<screen>
-				<![CDATA[
-spa     A la fecha tres calles bonaerenses recuerdan su nombre (en Ituzaingó, Merlo y Campana). A la fecha, unas 50 \
+<![CDATA[spa     A la fecha tres calles bonaerenses recuerdan su nombre (en Ituzaingó, Merlo y Campana). A la fecha, unas 50 \
 		naves y 20 aviones se han perdido en esa área particular del océano Atlántico.
 deu     Alle Jahre wieder: Millionen Spanier haben am Dienstag die Auslosung in der größten Lotterie der Welt verfolgt.\
  		Alle Jahre wieder: So gelingt der stressfreie Geschenke-Umtausch Artikel per E-Mail empfehlen So gelingt der \
@@ -149,8 +145,7 @@ lav     Egija Tri-Active procedūru īpaši iesaka izmantot siltākajos gadalaik
 			<para>
 				The following command will train the language detector and write the model to langdetect-custom.bin:
 				<screen>
-					<![CDATA[
-$ bin/opennlp LanguageDetectorTrainer[.leipzig] -model modelFile [-params paramsFile] \
+<![CDATA[$ bin/opennlp LanguageDetectorTrainer[.leipzig] -model modelFile [-params paramsFile] \
   [-factory factoryName] -data sampleData [-encoding charsetName]]]>
 				</screen>
 				<emphasis role="strong">Note</emphasis>: To customize the language detector, extend the class opennlp.tools.langdetect.LanguageDetectorFactory
@@ -173,8 +168,7 @@ $ bin/opennlp LanguageDetectorTrainer[.leipzig] -model modelFile [-params params
 				the following command shows how to train a model.
 
 				<screen>
-					<![CDATA[
-$ bin/opennlp LanguageDetectorTrainer.leipzig -model modelFile [-params paramsFile] [-factory factoryName] \
+<![CDATA[$ bin/opennlp LanguageDetectorTrainer.leipzig -model modelFile [-params paramsFile] [-factory factoryName] \
 	-sentencesDir sentencesDir -sentencesPerSample sentencesPerSample -samplesPerLanguage samplesPerLanguage \
 	[-encoding charsetName]]]>
 				</screen>
@@ -186,8 +180,7 @@ $ bin/opennlp LanguageDetectorTrainer.leipzig -model modelFile [-params paramsFi
 				and limiting to 10000 documents per language. Then, it shuffles the result and select the first
 				100000 lines as train corpus and the last 20000 as evaluation corpus:
 				<screen>
-					<![CDATA[
-$ bin/opennlp LanguageDetectorConverter leipzig -sentencesDir leipzig-train/ -sentencesPerSample 5 -samplesPerLanguage 10000 > leipzig.txt
+<![CDATA[$ bin/opennlp LanguageDetectorConverter leipzig -sentencesDir leipzig-train/ -sentencesPerSample 5 -samplesPerLanguage 10000 > leipzig.txt
 $ perl -MList::Util=shuffle -e 'print shuffle(<STDIN>);' < leipzig.txt > leipzig_shuf.txt
 $ head -100000 < leipzig_shuf.txt > leipzig.train
 $ tail -20000 < leipzig_shuf.txt > leipzig.eval]]>
@@ -199,16 +192,13 @@ $ tail -20000 < leipzig_shuf.txt > leipzig.eval]]>
 		<para>
 		The following example shows how to train a model from API.
 		<programlisting language="java">
-						<![CDATA[
-InputStreamFactory inputStreamFactory = new MarkableFileInputStreamFactory(new File("corpus.txt"));
+<![CDATA[InputStreamFactory inputStreamFactory = new MarkableFileInputStreamFactory(new File("corpus.txt"));
 
-ObjectStream<String> lineStream =
-  new PlainTextByLineStream(inputStreamFactory, StandardCharsets.UTF_8);
+ObjectStream<String> lineStream = new PlainTextByLineStream(inputStreamFactory, StandardCharsets.UTF_8);
 ObjectStream<LanguageSample> sampleStream = new LanguageDetectorSampleStream(lineStream);
 
 TrainingParameters params = ModelUtil.createDefaultTrainingParameters();
-params.put(TrainingParameters.ALGORITHM_PARAM,
-  PerceptronTrainer.PERCEPTRON_VALUE);
+params.put(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE);
 params.put(TrainingParameters.CUTOFF_PARAM, 0);
 
 LanguageDetectorFactory factory = new LanguageDetectorFactory();
diff --git a/opennlp-docs/src/docbkx/lemmatizer.xml b/opennlp-docs/src/docbkx/lemmatizer.xml
index 90264dbe7..fff1e0820 100644
--- a/opennlp-docs/src/docbkx/lemmatizer.xml
+++ b/opennlp-docs/src/docbkx/lemmatizer.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor 
 	license agreements. See the NOTICE file distributed with this work for additional 
@@ -15,20 +15,16 @@
 
 <chapter id="tools.lemmatizer">
 	<title>Lemmatizer</title>
-		<para>
-			The lemmatizer returns, for a given word form (token) and Part of Speech
-			tag,
-			the dictionary form of a word, which is usually referred to as its
-			lemma. A token could
-			ambiguously be derived from several basic forms or dictionary words which is why
-			the
-			postag of the word is required to find the lemma. For example, the form
-			`show' may refer
-			to either the verb "to show" or to the noun "show".
-			Currently, OpenNLP implement statistical and dictionary-based lemmatizers.
-		</para>
 		<section id="tools.lemmatizer.tagging.cmdline">
 			<title>Lemmatizer Tool</title>
+			<para>
+				A lemmatizer returns, for a given word form (token) and Part of Speech
+				tag, the dictionary form of a word, which is usually referred to as its
+				lemma. A token could ambiguously be derived from several basic forms or dictionary
+				words which is why the postag of the word is required to find the lemma.
+				For example, the form `show' may refer to either the verb "to show" or to the noun "show".
+				Currently, OpenNLP implement statistical and dictionary-based lemmatizers.
+			</para>
 			<para>
 				The easiest way to try out the Lemmatizer is the command line tool,
 				which provides access to the statistical
@@ -40,15 +36,13 @@
 			</para>
 			<para>
 				<screen>
-		   <![CDATA[
-$ opennlp LemmatizerME opennlp-en-ud-ewt-lemmas-1.3-2.5.4.bin < sentences]]>
+<![CDATA[$ opennlp LemmatizerME opennlp-en-ud-ewt-lemmas-1.3-2.5.4.bin < sentences]]>
 		  </screen>
 				The Lemmatizer now reads a pos tagged sentence(s) per line from
 				standard input. For example, you can copy this sentence to the
 				console:
 				<screen>
-		    <![CDATA[
-Rockwell_PROPN International_ADJ Corp_NOUN 's_PUNCT Tulsa_PROPN unit_NOUN said_VERB it_PRON
+<![CDATA[Rockwell_PROPN International_ADJ Corp_NOUN 's_PUNCT Tulsa_PROPN unit_NOUN said_VERB it_PRON
 signed_VERB a_DET tentative_NOUN agreement_NOUN extending_VERB its_PRON contract_NOUN
 with_ADP Boeing_PROPN Co._NOUN to_PART provide_VERB structural_ADJ parts_NOUN for_ADP
 Boeing_PROPN 's_PUNCT 747_NUM jetliners_NOUN ._PUNCT]]>
@@ -56,8 +50,7 @@ Boeing_PROPN 's_PUNCT 747_NUM jetliners_NOUN ._PUNCT]]>
 				The Lemmatizer will now echo the lemmas for each word postag pair to
 				the console:
 				<screen>
-		    <![CDATA[
-Rockwell	PROPN	rockwell
+<![CDATA[Rockwell	PROPN	rockwell
 International	ADJ	international
 Corp	NOUN	corp
 's	PUNCT	's
@@ -66,8 +59,7 @@ unit	NOUN	unit
 said	VERB	say
 it	PRON	it
 signed	VERB	sign
-...
-]]>
+...]]>
 		  </screen>
 			</para>
 		</section>
@@ -87,17 +79,14 @@ signed	VERB	sign
 				into memory from disk or from another source.
 				In the example below it is loaded from disk:
 				<programlisting language="java">
-		<![CDATA[
-LemmatizerModel model = null;
+<![CDATA[LemmatizerModel model = null;
 try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-lemmas-1.3-2.5.4.bin"))) {
   model = new LemmatizerModel(modelIn);
-}
-]]>
+}]]>
 			</programlisting>
-				After the model is loaded a LemmatizerME can be instantiated.
+				Once the model is loaded, a LemmatizerME can be instantiated.
 				<programlisting language="java">
-				<![CDATA[
-LemmatizerME lemmatizer = new LemmatizerME(model);]]>
+<![CDATA[LemmatizerME lemmatizer = new LemmatizerME(model);]]>
 			</programlisting>
 				The Lemmatizer instance is now ready to lemmatize data. It expects a
 				tokenized sentence
@@ -109,8 +98,7 @@ LemmatizerME lemmatizer = new LemmatizerME(model);]]>
 				The following code shows how to determine the most likely lemma for
 				a sentence.
 				<programlisting language="java">
-		  <![CDATA[
-String[] tokens = new String[] { "Rockwell", "International", "Corp.", "'s",
+<![CDATA[String[] tokens = new String[] { "Rockwell", "International", "Corp.", "'s",
     "Tulsa", "unit", "said", "it", "signed", "a", "tentative", "agreement",
     "extending", "its", "contract", "with", "Boeing", "Co.", "to",
     "provide", "structural", "parts", "for", "Boeing", "'s", "747",
@@ -135,8 +123,7 @@ String[] lemmas = lemmatizer.lemmatize(tokens, postags);]]>
 				consists of a text file containing, for each row, a word, its postag and the
 				corresponding lemma, each column separated by a tab character.
 				<screen>
-		<![CDATA[
-show		NOUN	show
+<![CDATA[show		NOUN	show
 showcase	NOUN	showcase
 showcases	NOUN	showcase
 showdown	NOUN	showdown
@@ -149,52 +136,40 @@ showmen		NOUN	showman
 showroom	NOUN	showroom
 showrooms	NOUN	showroom
 shows		NOUN	show
-shrapnel	NOUN	shrapnel
-		]]>
+shrapnel	NOUN	shrapnel]]>
 		</screen>
 				Alternatively, if a (word,postag) pair can output multiple lemmas, the
 				the lemmatizer dictionary would consist of a text file containing, for
 				each row, a word, its postag and the corresponding lemmas separated by "#":
 				<screen>
-		<![CDATA[
-muestras	NOUN	muestra
+<![CDATA[muestras	NOUN	muestra
 cantaba		VERB	cantar
 fue		VERB	ir#ser
-entramos	VERB	entrar
-		]]>
+entramos	VERB	entrar]]>
 					</screen>
 				First the dictionary must be loaded into memory from disk or another
 				source.
 				In the sample below it is loaded from disk.
 				<programlisting language="java">
-				<![CDATA[
-InputStream dictLemmatizer = null;
-
+<![CDATA[InputStream dictLemmatizer = null;
 try (dictLemmatizer = new FileInputStream("english-dict-lemmatizer.txt")) {
 
-}
-]]>
+}]]>
 			</programlisting>
 				After the dictionary is loaded the DictionaryLemmatizer can be
 				instantiated.
 				<programlisting language="java">
-			  <![CDATA[
-DictionaryLemmatizer lemmatizer = new DictionaryLemmatizer(dictLemmatizer);]]>
+<![CDATA[DictionaryLemmatizer lemmatizer = new DictionaryLemmatizer(dictLemmatizer);]]>
 			</programlisting>
 				The DictionaryLemmatizer instance is now ready. It expects two
-				String arrays as input,
-				a containing the tokens and another one their respective postags.
-			</para>
-			<para>
-				The following code shows how to find a lemma using a
-				DictionaryLemmatizer.
+				String arrays as input, containing the tokens and their respective postags.
+				The following code shows how to find a lemma using a DictionaryLemmatizer.
+				
 				<programlisting language="java">
-		  <![CDATA[
-String[] tokens = new String[]{"Most", "large", "cities", "in", "the", "US", "had",
+<![CDATA[String[] tokens = new String[]{"Most", "large", "cities", "in", "the", "US", "had",
                              "morning", "and", "afternoon", "newspapers", "."};
 String[] tags = tagger.tag(sent);
-String[] lemmas = lemmatizer.lemmatize(tokens, postags);
-]]>
+String[] lemmas = lemmatizer.lemmatize(tokens, tags);]]>
 			</programlisting>
 				The tags array contains one part-of-speech tag for each token in the
 				input array. The corresponding
@@ -206,18 +181,13 @@ String[] lemmas = lemmatizer.lemmatize(tokens, postags);
 			<title>Lemmatizer Training</title>
 			<para>
 				The training data consist of three columns separated by tabs. Each
-				word has been put on a
-				separate line and there is an empty line after each sentence. The first
-				column contains
-				the current word, the second its part-of-speech tag and the third its
-				lemma.
-				Here is an example of the file format:
+				word has been put on a separate line and there is an empty line after each sentence. The first
+				column contains the current word, the second its part-of-speech tag and the third its lemma.
+				Here is an example of the file format, with a sample sentence of the training data:
 			</para>
 			<para>
-				Sample sentence of the training data:
 				<screen>
-		<![CDATA[
-He        PRON  he
+<![CDATA[He        PRON  he
 reckons   VERB  reckon
 the       DET   the
 current   ADJ   current
@@ -246,32 +216,30 @@ September PROPN  september
 				<para>
 					Usage of the tool:
 					<screen>
-		<![CDATA[
-$ opennlp LemmatizerTrainerME
+<![CDATA[$ opennlp LemmatizerTrainerME
 Usage: opennlp LemmatizerTrainerME [-factory factoryName] [-params paramsFile] -lang language -model modelFile -data sampleData [-encoding charsetName]
 
 Arguments description:
-	-factory factoryName
-		A sub-class of LemmatizerFactory where to get implementation and resources.
-	-params paramsFile
-		training parameters file.
-	-lang language
-		language which is being processed.
-	-model modelFile
-		output model file.
-	-data sampleData
-		data to be used, usually a file name.
-	-encoding charsetName
-	encoding for reading and writing text, if absent the system default is used.
-		]]>
+		-factory factoryName
+				A sub-class of LemmatizerFactory where to get implementation and resources.
+		-params paramsFile
+				training parameters file.
+		-lang language
+				language which is being processed.
+		-model modelFile
+				output model file.
+		-data sampleData
+				data to be used, usually a file name.
+		-encoding charsetName
+				encoding for reading and writing text, if absent the system default is used.]]>
 		</screen>
 					Its now assumed that the english lemmatizer model should be trained
 					from a file called
 					'en-custom-lemmatizer.train' which is encoded as UTF-8. The following command will train the
 					lemmatizer and write the model to en-custom-lemmatizer.bin:
 					<screen>
-		<![CDATA[
-$ opennlp LemmatizerTrainerME -model en-custom-lemmatizer.bin -params PerceptronTrainerParams.txt -lang en -data en-custom-lemmatizer.train -encoding UTF-8]]>
+<![CDATA[$ opennlp LemmatizerTrainerME -model en-custom-lemmatizer.bin -params PerceptronTrainerParams.txt \
+																			 					-lang en -data en-custom-lemmatizer.train -encoding UTF-8]]>
 		</screen>
 				</para>
 			</section>
@@ -282,52 +250,42 @@ $ opennlp LemmatizerTrainerME -model en-custom-lemmatizer.bin -params Perceptron
 					a training parameters
 					file needs to be instantiated:
 					<programlisting language="java">
-                    <![CDATA[
- TrainingParameters mlParams = CmdLineUtil.loadTrainingParameters(params.getParams(), false);
- if (mlParams == null) {
-   mlParams = ModelUtil.createDefaultTrainingParameters();
- }]]>
+<![CDATA[TrainingParameters mlParams = CmdLineUtil.loadTrainingParameters(params.getParams(), false);
+if (mlParams == null) {
+		mlParams = ModelUtil.createDefaultTrainingParameters();
+}]]>
                 </programlisting>
 					Then we read the training data:
 					<programlisting language="java">
-                    <![CDATA[
-InputStreamFactory inputStreamFactory = null;
-    try {
-      inputStreamFactory = new MarkableFileInputStreamFactory(
-          new File(en-custom-lemmatizer.train));
-    } catch (FileNotFoundException e) {
-      e.printStackTrace();
-    }
-    ObjectStream<String> lineStream = null;
-    LemmaSampleStream lemmaStream = null;
-    try {
-      lineStream = new PlainTextByLineStream(
-      (inputStreamFactory), StandardCharsets.UTF_8);
-      lemmaStream = new LemmaSampleStream(lineStream);
-    } catch (IOException e) {
-      CmdLineUtil.handleCreateObjectStreamError(e);
-    }
-]]>
+<![CDATA[InputStreamFactory inputStreamFactory = null;
+try {
+		inputStreamFactory = new MarkableFileInputStreamFactory(new File(en-custom-lemmatizer.train));
+} catch (FileNotFoundException e) {
+		e.printStackTrace();
+}
+ObjectStream<String> lineStream = null;
+LemmaSampleStream lemmaStream = null;
+try {
+		lineStream = new PlainTextByLineStream((inputStreamFactory), StandardCharsets.UTF_8);
+		lemmaStream = new LemmaSampleStream(lineStream);
+} catch (IOException e) {
+		CmdLineUtil.handleCreateObjectStreamError(e);
+}]]>
                 </programlisting>
 					The following step proceeds to train the model:
-					<programlisting>
-    LemmatizerModel model;
-    try {
-      LemmatizerFactory lemmatizerFactory = LemmatizerFactory
-          .create(params.getFactory());
-      model = LemmatizerME.train(params.getLang(), lemmaStream, mlParams,
-          lemmatizerFactory);
-    } catch (IOException e) {
-      throw new TerminateToolException(-1,
-          "IO error while reading training data or indexing data: "
-              + e.getMessage(),
-          e);
-    } finally {
-      try {
-        sampleStream.close();
-      } catch (IOException e) {
-      }
-    }
+					<programlisting language="java">
+<![CDATA[LemmatizerModel model;
+try {
+		LemmatizerFactory lemmatizerFactory = LemmatizerFactory.create(params.getFactory());
+		model = LemmatizerME.train(params.getLang(), lemmaStream, mlParams, lemmatizerFactory);
+} catch (IOException e) {
+		throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.getMessage(), e);
+} finally {
+		try {
+				sampleStream.close();
+		} catch (IOException e) {
+		}
+}]]>
 		</programlisting>
 				</para>
 			</section>
@@ -344,13 +302,11 @@ InputStreamFactory inputStreamFactory = null;
 					data set.
 					The following command shows how the tool can be run:
 					<screen>
-				<![CDATA[
-$ opennlp LemmatizerEvaluator -model en-custom-lemmatizer.bin -data en-custom-lemmatizer.test -encoding utf-8]]>
+<![CDATA[$ opennlp LemmatizerEvaluator -model en-custom-lemmatizer.bin -data en-custom-lemmatizer.test -encoding utf-8]]>
 			 </screen>
 					This will display the resulting accuracy score, e.g.:
 					<screen>
-				<![CDATA[
-Loading model ... done
+<![CDATA[Loading model ... done
 Evaluating ... done
 
 Accuracy: 0.9659110277825124]]>
diff --git a/opennlp-docs/src/docbkx/machine-learning.xml b/opennlp-docs/src/docbkx/machine-learning.xml
index a42e7be10..375152264 100644
--- a/opennlp-docs/src/docbkx/machine-learning.xml
+++ b/opennlp-docs/src/docbkx/machine-learning.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -67,8 +67,8 @@ under the License.
 		provides an excellent introduction and discussion of the framework.
 		</para>
 		<para>
-		*Foundations of statistical natural language processing . Christopher D. Manning, Hinrich Schütze.
-		Cambridge, Mass. : MIT Press, c1999.
+		*Foundations of statistical natural language processing. Christopher D. Manning, Hinrich Schütze.
+		Cambridge, Mass.: MIT Press, c1999.
 		</para>
 			<section id="opennlp.ml.maxent.impl">
 		<title>Implementation</title>
diff --git a/opennlp-docs/src/docbkx/model-loading.xml b/opennlp-docs/src/docbkx/model-loading.xml
index e901abf76..6af9e23ee 100644
--- a/opennlp-docs/src/docbkx/model-loading.xml
+++ b/opennlp-docs/src/docbkx/model-loading.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-        "http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+        "https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
         ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -23,6 +23,9 @@ under the License.
 
 <chapter id="tools.model">
     <title>Classpath Loading of OpenNLP Models</title>
+
+  <section id="tools.model.background">
+    <title>Background</title>
     <para>
         Since version 2.4.0, OpenNLP supports the ability to load pre-trained OpenNLP models from the classpath.
         It relies on either a simple implementation using the application's classpath or on the
@@ -31,11 +34,11 @@ under the License.
         Our pre-trained models are bundled from the <ulink url="https://github.com/apache/opennlp-models">OpenNLP Models
         repository</ulink>.
 
-        This section describes
+        This chapter describes
 
         <itemizedlist>
             <listitem>
-                <para>how to load and use a pre-trained OpenNLP model from the classpath.</para>
+                <para>how to load and use a pre-trained OpenNLP model available in the classpath.</para>
             </listitem>
             <listitem>
                 <para>how to bundle a custom OpenNLP model to be loadable as a JAR file from the classpath.</para>
@@ -43,58 +46,51 @@ under the License.
         </itemizedlist>
 
     </para>
+  </section>
 
 	<section id="tools.model.load">
-		<title>Loading a pre-trained OpenNLP model from the classpath</title>
+		<title>Loading a pre-trained model</title>
         <para>
             First, you need to add the following dependency to your classpath:
 
             <programlisting language="xml">
-                <![CDATA[
-<dependency>
+<![CDATA[<dependency>
   <groupId>org.apache.opennlp</groupId>
   <artifactId>opennlp-tools-models</artifactId>
   <version>CURRENT_OPENNLP_VERSION</version>
-</dependency>
-]]>
+</dependency>]]>
             </programlisting>
 
             by using our pre-trained models or by building custom models as described later in this section.
             If you need advanced classpath scanning capabilities, you can also add the classgraph library to your classpath.
 
             <programlisting language="xml">
-                <![CDATA[
-<dependency>
+<![CDATA[<dependency>
     <groupId>io.github.classgraph</groupId>
     <artifactId>classgraph</artifactId>
     <version>CURRENT_CLASSGRAPH_VERSION</version>
-</dependency>
-]]>
+</dependency>]]>
             </programlisting>
 
             Make sure you replace the placeholders with the appropriate version values.
-
             Next, you can search for such a model and load it from the classpath:
 
             <programlisting language="java">
-                <![CDATA[
-final ClassgraphModelFinder finder = new ClassgraphModelFinder(); // or use: new SimpleClassPathModelFinder()
+<![CDATA[final ClassgraphModelFinder finder = new ClassgraphModelFinder(); // or use: new SimpleClassPathModelFinder()
 final ClassPathModelLoader loader = new ClassPathModelLoader();
 final Set<ClassPathModelEntry> models = finder.findModels(false);
 for(ClassPathModelEntry entry : models) {
 
-   final ClassPathModel model = loader.load(entry);
-
-   if(model != null) {
-      System.out.println(model.getModelName());
-      System.out.println(model.getModelSHA256());
-      System.out.println(model.getModelVersion());
-      System.out.println(model.getModelLanguage());
-      // do something with the model by consuming the byte array
-   }
+     final ClassPathModel model = loader.load(entry);
+     if(model != null) {
+          System.out.println(model.getModelName());
+          System.out.println(model.getModelSHA256());
+          System.out.println(model.getModelVersion());
+          System.out.println(model.getModelLanguage());
+          // do something with the model by consuming the byte array
+     }
 }]]>
             </programlisting>
-
         </para>
         <para>
           Moreover, certain OpenNLP models can be obtained via a
@@ -104,36 +100,32 @@ for(ClassPathModelEntry entry : models) {
           that those are present in the classpath and can be loaded.
 
           <programlisting language="java">
-            <![CDATA[
-final ClassPathModelProvider provider = new DefaultClassPathModelProvider(finder, loader);
+<![CDATA[final ClassPathModelProvider provider = new DefaultClassPathModelProvider(finder, loader);
 // Here: SentenceModel, other model types accordingly
 final SentenceModel sm = provider.load("en", opennlp.tools.models.ModelType.SENTENCE_DETECTOR, SentenceModel.class);
 if(sm != null) {
-  // do something with the (sentence) model
+    // do something with the (sentence) model
 }]]>
           </programlisting>
 
-          In the above example, the finder and loader objects can be created or re-used as shown in the
-          previous code example.
+          In this example, the finder and loader objects can be created or re-used as shown in the previous code example.
+          <emphasis role="strong">Note</emphasis>: When running on Java 17+, the JVM argument
         </para>
         <para>
-          <emphasis role="strong">Note</emphasis>:
-          When running on Java 17+, the JVM argument
-          
-          <screen>--add-opens java.base/jdk.internal.loader=ALL-UNNAMED</screen>
+          <screen>
+<![CDATA[--add-opens java.base/jdk.internal.loader=ALL-UNNAMED]]>
+          </screen>
 
           may be required. Without this parameter, OpenNLP uses the JVM bootstrap classpath to locate models
           rather than the UCP class loader.
-
           For more advanced or non-standard class loading scenarios, using ClassGraph and implementing a
           custom provider may cover additional cases beyond the default UCP class loader or
           JVM bootstrap class path behavior.
         </para>
 	</section>
 
-
 	<section id="tools.model.bundle">
-		<title>Bundling a custom trained OpenNLP model for the classpath</title>
+		<title>Bundling a self-trained model</title>
 		<para>
             If you intend to provide your own custom trained OpenNLP models as JAR files for classpath discovery,
             we recommend that you have a look at our setup in the <ulink url="https://github.com/apache/opennlp-models">OpenNLP Models
@@ -142,12 +134,10 @@ if(sm != null) {
             Make sure you add a <emphasis>model.properties</emphasis> file with the following content:
 
        <programlisting language="java">
-                <![CDATA[
-model.name=${model.name}
+<![CDATA[model.name=${model.name}
 model.version=${model.version}
 model.sha256=${model.sha256}
-model.language=${model.language}
-]]>
+model.language=${model.language}]]>
             </programlisting>
 
             Make sure to replace the values accordingly and configure your build tool to include the binary model and the <emphasis>model.properties</emphasis>
@@ -157,21 +147,18 @@ model.language=${model.language}
             you need the following code to successfully find and load it:
 
             <programlisting language="java">
-                <![CDATA[
-final ClassgraphModelFinder finder = new ClassgraphModelFinder("custom-opennlp-model.jar"); // or use: new SimpleClassPathModelFinder("custom-opennlp-model.jar")
+<![CDATA[final ClassgraphModelFinder finder = new ClassgraphModelFinder("custom-opennlp-model.jar"); // or use: new SimpleClassPathModelFinder("custom-opennlp-model.jar")
 final ClassPathModelLoader loader = new ClassPathModelLoader();
 final Set<ClassPathModelEntry> models = finder.findModels(false);
 for(ClassPathModelEntry entry : models) {
-
-   final ClassPathModel model = loader.load(entry);
-
-   if(model != null) {
+    final ClassPathModel model = loader.load(entry);
+    if(model != null) {
       System.out.println(model.getModelName());
       System.out.println(model.getModelSHA256());
       System.out.println(model.getModelVersion());
       System.out.println(model.getModelLanguage());
       // do something with the model by consuming the byte array
-   }
+    }
 }]]>
             </programlisting>
 
diff --git a/opennlp-docs/src/docbkx/morfologik-addon.xml b/opennlp-docs/src/docbkx/morfologik-addon.xml
index 27c2dcd0c..08e352fe9 100644
--- a/opennlp-docs/src/docbkx/morfologik-addon.xml
+++ b/opennlp-docs/src/docbkx/morfologik-addon.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor 
 	license agreements. See the NOTICE file distributed with this work for additional 
@@ -54,9 +54,10 @@
 		<code>pt-morfologik.dict</code>. It will output a model named <code>pos-pt_fsadic.model</code>.
 		
 		<screen>
-		<![CDATA[
-$ bin/opennlp POSTaggerTrainer -type perceptron -lang pt -model pos-pt_fsadic.model -data portuguese_bosque_train.conll \
-	 -encoding UTF-8 -factory opennlp.morfologik.tagdict.MorfologikPOSTaggerFactory -dict pt-morfologik.dict]]>
+<![CDATA[$ bin/opennlp POSTaggerTrainer -type perceptron -lang pt -model pos-pt_fsadic.model \
+																				-data portuguese_bosque_train.conll -encoding UTF-8 \
+																				-factory opennlp.morfologik.tagdict.MorfologikPOSTaggerFactory \
+																				-dict pt-morfologik.dict]]>
 		</screen>
 		
 		</para>
@@ -67,8 +68,7 @@ $ bin/opennlp POSTaggerTrainer -type perceptron -lang pt -model pos-pt_fsadic.mo
 		<para>
 		File <code>lemmaDictionary.txt:</code>
 		<screen>
-		<![CDATA[
-casa,casa,NOUN
+<![CDATA[casa,casa,NOUN
 casar,casa,V
 casar,casar,V-INF
 Casa,Casa,PROP
@@ -84,8 +84,7 @@ carro,carro,NOUN]]>
 		<para>
 		Mandatory metadata file, which must have the same name but .info extension <code>lemmaDictionary.info:</code>
 		<screen>
-		<![CDATA[
-#
+<![CDATA[#
 # REQUIRED PROPERTIES
 #
 
@@ -99,8 +98,7 @@ fsa.dict.encoding=UTF-8
 # construction. Allowed values: [suffix, infix, prefix, none].
 # Details are in Daciuk's paper and in the code. 
 # Leave at 'prefix' if not sure.
-fsa.dict.encoder=prefix
-		]]>
+fsa.dict.encoder=prefix]]>
 		</screen>
 		</para>
 		<para>
@@ -108,8 +106,7 @@ fsa.dict.encoder=prefix
 		find the lemma the word "casa" noun and verb.
 		
 				<programlisting language="java">
-		<![CDATA[
-// Part 1: compile an FSA lemma dictionary
+<![CDATA[// Part 1: compile an FSA lemma dictionary
    
 // we need the tabular dictionary. It is mandatory to have info 
 //  file with same name, but .info extension
@@ -126,8 +123,7 @@ String[] toks = {"casa", "casa"};
 String[] tags = {"NOUN", "V"};
 
 String[] lemmas = lemmatizer.lemmatize(toks, tags);
-System.out.println(Arrays.toString(lemmas)); // outputs [casa, casar]
-    ]]>
+System.out.println(Arrays.toString(lemmas)); // outputs [casa, casar]]]>
 			</programlisting>
 		
 		</para>
@@ -139,15 +135,13 @@ System.out.println(Arrays.toString(lemmas)); // outputs [casa, casar]
 				to a tabular format. <code>MorfologikDictionaryBuilder</code> can take a tabular dictionary and output a binary Morfologik FSA dictionary.
 			</para>
 			<screen>
-		<![CDATA[
-$ sh bin/morfologik-addon
+<![CDATA[$ sh bin/morfologik-addon
 OpenNLP Morfologik Addon. Usage: opennlp-morfologik-addon TOOL
 where TOOL is one of:
   MorfologikDictionaryBuilder    builds a binary POS Dictionary using Morfologik
   XMLDictionaryToTable           reads an OpenNLP XML tag dictionary and outputs it in a tabular file
 All tools print help when invoked with help parameter
-Example: opennlp-morfologik-addon POSDictionaryBuilder help
-		]]>
+Example: opennlp-morfologik-addon POSDictionaryBuilder help]]>
 		</screen>
 		</section>
 </chapter>
\ No newline at end of file
diff --git a/opennlp-docs/src/docbkx/namefinder.xml b/opennlp-docs/src/docbkx/namefinder.xml
index 284a4a316..f69a7fdbc 100644
--- a/opennlp-docs/src/docbkx/namefinder.xml
+++ b/opennlp-docs/src/docbkx/namefinder.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -46,8 +46,7 @@ under the License.
 			English
 			person model and start the Name Finder Tool with this command:
 			<screen>
-				<![CDATA[
-$ opennlp TokenNameFinder en-ner-person.bin]]>
+<![CDATA[$ opennlp TokenNameFinder en-ner-person.bin]]>
 			 </screen>
 			 
 			The name finder now reads a tokenized sentence per line from stdin, an empty
@@ -55,19 +54,17 @@ $ opennlp TokenNameFinder en-ner-person.bin]]>
 			Just copy this text to the terminal:
 	
 			<screen>
-				<![CDATA[
-Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
+<![CDATA[Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
 Mr . Vinken is chairman of Elsevier N.V. , the Dutch publishing group .
 Rudolph Agnew , 55 years old and former chairman of Consolidated Gold Fields PLC , was named
-    a director of this British industrial conglomerate .]]>
+	a director of this British industrial conglomerate .]]>
 			 </screen>
 			 the name finder will now output the text with markup for person names:
 			<screen>
-				<![CDATA[
-<START:person> Pierre Vinken <END> , 61 years old , will join the board as a nonexecutive director Nov. 29 .
+<![CDATA[<START:person> Pierre Vinken <END> , 61 years old , will join the board as a nonexecutive director Nov. 29 .
 Mr . <START:person> Vinken <END> is chairman of Elsevier N.V. , the Dutch publishing group .
 <START:person> Rudolph Agnew <END> , 55 years old and former chairman of Consolidated Gold Fields PLC ,
-    was named a director of this British industrial conglomerate .]]>
+	was named a director of this British industrial conglomerate .]]>
 			 </screen>
 		</para>
 	</section>
@@ -79,8 +76,7 @@ Mr . <START:person> Vinken <END> is chairman of Elsevier N.V. , the Dutch publis
 			First the name finder model must be loaded into memory from disk or another source.
 			In the sample below it is loaded from disk.
 			<programlisting language="java">
-				<![CDATA[
-try (InputStream modelIn = new FileInputStream("en-ner-person.bin")){
+<![CDATA[try (InputStream modelIn = new FileInputStream("en-ner-person.bin")){
   TokenNameFinderModel model = new TokenNameFinderModel(modelIn);
 }
 ]]>
@@ -103,8 +99,7 @@ try (InputStream modelIn = new FileInputStream("en-ner-person.bin")){
 			</itemizedlist>
 			After the model is loaded the NameFinderME can be instantiated.
 			<programlisting language="java">
-				<![CDATA[
-NameFinderME nameFinder = new NameFinderME(model);]]>
+<![CDATA[NameFinderME nameFinder = new NameFinderME(model);]]>
 			</programlisting>
 			The initialization is now finished and the Name Finder can be used. The NameFinderME
 			class is not thread safe, it must only be called from one thread. To use multiple threads
@@ -116,21 +111,17 @@ NameFinderME nameFinder = new NameFinderME(model);]]>
 			rate after a few documents.
 			The following code illustrates that:
 			<programlisting language="java">
-				<![CDATA[
-for (String document[][] : documents) {
-
+<![CDATA[for (String document[][] : documents) {
   for (String[] sentence : document) {
     Span nameSpans[] = nameFinder.find(sentence);
     // do something with the names
   }
-
   nameFinder.clearAdaptiveData()
 }]]>
 			 </programlisting>
 			 the following snippet shows a call to find
 			 <programlisting language="java">
-				<![CDATA[
-String[] sentence = new String[]{
+<![CDATA[String[] sentence = new String[]{
     "Pierre",
     "Vinken",
     "is",
@@ -157,8 +148,7 @@ Span[] nameSpans = nameFinder.find(sentence);]]>
 					(There is no need to load the model as an InputStream as in the previous example.) The name finder
 					requires a tokenized list of strings as input. The output will be an array of spans.
 					<programlisting language="java">
-						<![CDATA[
-File model = new File("/path/to/model.onnx");
+<![CDATA[File model = new File("/path/to/model.onnx");
 File vocab = new File("/path/to/vocab.txt");
 Map<Integer, String> categories = new HashMap<>();
 String[] tokens = new String[]{"George", "Washington", "was", "president", "of", "the", "United", "States", "."};
@@ -192,7 +182,7 @@ Span[] spans = nameFinderDL.find(tokens);]]>
 		</para>
 		<para>
 			The data can be converted to the OpenNLP name finder training format. Which is one
-            sentence per line. Some other formats are available as well.
+			sentence per line. Some other formats are available as well.
 			The sentence must be tokenized and contain spans which mark the entities. Documents are separated by
 			empty lines which trigger the reset of the adaptive feature generators. A training file can contain
 			multiple types. If the training file contains multiple types the created model will also be able to
@@ -201,15 +191,13 @@ Span[] spans = nameFinderDL.find(tokens);]]>
 		<para>
 			Sample sentence of the data:
 			<screen>
-				<![CDATA[
-<START:person> Pierre Vinken <END> , 61 years old , will join the board as a nonexecutive director Nov. 29 .
+<![CDATA[<START:person> Pierre Vinken <END> , 61 years old , will join the board as a nonexecutive director Nov. 29 .
 Mr . <START:person> Vinken <END> is chairman of Elsevier N.V. , the Dutch publishing group .]]>
 			 </screen>
 			 The training data should contain at least 15000 sentences to create a model which performs well.
 			 Usage of the tool:
 			<screen>
-				<![CDATA[
-$ opennlp TokenNameFinderTrainer
+<![CDATA[$ opennlp TokenNameFinderTrainer
 Usage: opennlp TokenNameFinderTrainer[.evalita|.ad|.conll03|.bionlp2004|.conll02|.muc6|.ontonotes|.brat] \
 [-featuregen featuregenFile] [-nameTypes types] [-sequenceCodec codec] [-factory factoryName] \
 [-resources resourcesDir] [-type typeOverride] [-params paramsFile] -lang language \
@@ -243,8 +231,7 @@ Arguments description:
 			 called 'en-ner-person.train' which is encoded as UTF-8. The following command will train
 			 the name finder and write the model to en-ner-person.bin:
 			 <screen>
-				<![CDATA[
-$ opennlp TokenNameFinderTrainer -model en-ner-person.bin -lang en -data en-ner-person.train -encoding UTF-8]]>
+<![CDATA[$ opennlp TokenNameFinderTrainer -model en-ner-person.bin -lang en -data en-ner-person.train -encoding UTF-8]]>
 			 </screen>
 The example above will train models with a pre-defined feature set. It is also possible to use the -resources parameter to generate features based on external knowledge such as those based on word representation (clustering) features. The external resources must all be placed in a resource directory which is then passed as a parameter. If this option is used it is then required to pass, via the -featuregen parameter, an XML custom feature generator which includes some clustering features shipped with the TokenNameFinder. Currently, three formats of clustering lexicons are accepted:
 			<itemizedlist>
@@ -260,10 +247,12 @@ The example above will train models with a pre-defined feature set. It is also p
 				</listitem>
 			</itemizedlist>
 			 Additionally it is possible to specify the number of iterations,
-			 the cutoff and to overwrite all types in the training data with a single type. Finally, the -sequenceCodec parameter allows to specify a BIO (Begin, Inside, Out) or BILOU (Begin, Inside, Last, Out, Unit) encoding to represent the Named Entities. An example of one such command would be as follows:
+			 the cutoff and to overwrite all types in the training data with a single type.
+			Finally, the -sequenceCodec parameter allows to specify a BIO (Begin, Inside, Out) or
+			BILOU (Begin, Inside, Last, Out, Unit) encoding to represent the Named Entities.
+			An example of one such command would be as follows:
 			 <screen>
-			   <![CDATA[
-$ opennlp TokenNameFinderTrainer -featuregen brown.xml -sequenceCodec BILOU -resources clusters/ \
+<![CDATA[$ opennlp TokenNameFinderTrainer -featuregen brown.xml -sequenceCodec BILOU -resources clusters/ \
 -params PerceptronTrainerParams.txt -lang en -model ner-test.bin -data en-train.opennlp -encoding UTF-8]]>
 			 </screen>
 		</para>
@@ -287,8 +276,7 @@ $ opennlp TokenNameFinderTrainer -featuregen brown.xml -sequenceCodec BILOU -res
 			</itemizedlist>
 			The three steps are illustrated by the following sample code:
 			<programlisting language="java">
-				<![CDATA[
-TokenNameFinderFactory factory = TokenNameFinderFactory.create(null, null, Collections.emptyMap(), new BioCodec());
+<![CDATA[TokenNameFinderFactory factory = TokenNameFinderFactory.create(null, null, Collections.emptyMap(), new BioCodec());
 File trainingFile = new File("en-ner-person.train");
 ObjectStream<String> lineStream =
 				new PlainTextByLineStream(new MarkableFileInputStreamFactory(trainingFile), StandardCharsets.UTF_8);
@@ -321,17 +309,16 @@ try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(model
 				time is different the name finder might not be able to detect names.
 				The following lines show how to construct a custom feature generator
 				<programlisting language="java">
-					<![CDATA[
-AdaptiveFeatureGenerator featureGenerator = new CachedFeatureGenerator(
-         new AdaptiveFeatureGenerator[]{
-           new WindowFeatureGenerator(new TokenFeatureGenerator(), 2, 2),
-           new WindowFeatureGenerator(new TokenClassFeatureGenerator(true), 2, 2),
-           new OutcomePriorFeatureGenerator(),
-           new PreviousMapFeatureGenerator(),
-           new BigramNameFeatureGenerator(),
-           new SentenceFeatureGenerator(true, false),
-           new BrownTokenFeatureGenerator(BrownCluster dictResource)
-           });]]>
+<![CDATA[AdaptiveFeatureGenerator featureGenerator = new CachedFeatureGenerator(
+		 new AdaptiveFeatureGenerator[]{
+			 new WindowFeatureGenerator(new TokenFeatureGenerator(), 2, 2),
+			 new WindowFeatureGenerator(new TokenClassFeatureGenerator(true), 2, 2),
+			 new OutcomePriorFeatureGenerator(),
+			 new PreviousMapFeatureGenerator(),
+			 new BigramNameFeatureGenerator(),
+			 new SentenceFeatureGenerator(true, false),
+			 new BrownTokenFeatureGenerator(BrownCluster dictResource)
+		 });]]>
 				</programlisting>
 				which is similar to the default feature generator but with a BrownTokenFeature added.
 				The javadoc of the feature generator classes explain what the individual feature generators do.
@@ -339,31 +326,27 @@ AdaptiveFeatureGenerator featureGenerator = new CachedFeatureGenerator(
 				if it must not be adaptive extend the FeatureGeneratorAdapter.
 				The train method which should be used is defined as
 				<programlisting language="java">
-					<![CDATA[
-public static TokenNameFinderModel train(String languageCode, String type,
+<![CDATA[public static TokenNameFinderModel train(String languageCode, String type,
           ObjectStream<NameSample> samples, TrainingParameters trainParams,
           TokenNameFinderFactory factory) throws IOException]]>
 				</programlisting>
 				where the TokenNameFinderFactory allows to specify a custom feature generator.
 				To detect names the model which was returned from the train method must be passed to the NameFinderME constructor.
 				<programlisting language="java">
-					<![CDATA[
-new NameFinderME(model);]]>
+<![CDATA[new NameFinderME(model);]]>
 				 </programlisting>
 			</para>
 			</section>
 			<section id="tools.namefind.training.featuregen.xml">
 			<title>Feature Generation defined by XML Descriptor</title>
 			<para>
-			OpenNLP can also use a xml descriptor file to configure the feature generation. The
-            descriptor
+			OpenNLP can also use a xml descriptor file to configure the feature generation. The descriptor
 			file is stored inside the model after training and the feature generators are configured
 			correctly when the name finder is instantiated.
 
 			The following sample shows a xml descriptor which contains the default feature generator plus several types of clustering features:
 				<programlisting language="xml">
-					<![CDATA[
-<featureGenerators cache="true" name="nameFinder">
+<![CDATA[<featureGenerators cache="true" name="nameFinder">
   <generator class="opennlp.tools.util.featuregen.WindowFeatureGeneratorFactory">
     <int name="prevLength">2</int>
     <int name="nextLength">2</int>
@@ -499,8 +482,7 @@ new NameFinderME(model);]]>
 		<para>
 		The following command shows how the tool can be run:
 		<screen>
-            <![CDATA[
-$ opennlp TokenNameFinderEvaluator -model en-ner-person.bin -data en-ner-person.test -encoding UTF-8
+<![CDATA[$ opennlp TokenNameFinderEvaluator -model en-ner-person.bin -data en-ner-person.test -encoding UTF-8
 
 Precision: 0.8005071889818507
 Recall: 0.7450581122145297
@@ -516,20 +498,17 @@ F-Measure: 0.7717879983140168]]>
 		In the first case the model must be loaded and a NameSample ObjectStream must be created (see code samples above),
 		assuming these two objects exist the following code shows how to perform the evaluation:
 			<programlisting language="java">
-				<![CDATA[
-TokenNameFinderEvaluator evaluator = new TokenNameFinderEvaluator(new NameFinderME(model));
+<![CDATA[TokenNameFinderEvaluator evaluator = new TokenNameFinderEvaluator(new NameFinderME(model));
 evaluator.evaluate(sampleStream);
 
 FMeasure result = evaluator.getFMeasure();
-
 System.out.println(result.toString());]]>
 			</programlisting>
 			In the cross validation case all the training arguments must be
 			provided (see the Training API section above).
 			To perform cross validation the ObjectStream must be resettable.
 			<programlisting language="java">
-				<![CDATA[
-InputStreamFactory dataIn = new MarkableFileInputStreamFactory(new File("en-ner-person.train"));
+<![CDATA[InputStreamFactory dataIn = new MarkableFileInputStreamFactory(new File("en-ner-person.train"));
 ObjectStream<NameSample> sampleStream = new NameSampleDataStream(
     new PlainTextByLineStream(dataIn, StandardCharsets.UTF_8));
 TokenNameFinderCrossValidator evaluator = new TokenNameFinderCrossValidator("eng",
@@ -537,7 +516,6 @@ TokenNameFinderCrossValidator evaluator = new TokenNameFinderCrossValidator("eng
 evaluator.evaluate(sampleStream, 10);
 
 FMeasure result = evaluator.getFMeasure();
-
 System.out.println(result.toString());]]>
 			</programlisting>
 		</para>
@@ -553,21 +531,21 @@ System.out.println(result.toString());]]>
 				<itemizedlist>
 				<listitem>
 					<para>
-						<ulink  url="https://cs.nyu.edu/cs/faculty/grishman/NEtask20.book_1.html">
+						<ulink url="https://cs.nyu.edu/cs/faculty/grishman/NEtask20.book_1.html">
 							MUC6
 						</ulink>
 					</para>
 				</listitem>
 				<listitem>
 					<para>
-						<ulink  url="http://acl.ldc.upenn.edu/muc7/ne_task.html">
+						<ulink url="http://acl.ldc.upenn.edu/muc7/ne_task.html">
 							MUC7
 						</ulink>
 					</para>
 				</listitem>
 				<listitem>
 					<para>
-						<ulink  url="https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf">
+						<ulink url="https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf">
 							ACE
 						</ulink>
 					</para>
diff --git a/opennlp-docs/src/docbkx/opennlp.xml b/opennlp-docs/src/docbkx/opennlp.xml
index f07769e1c..59d7c1d41 100644
--- a/opennlp-docs/src/docbkx/opennlp.xml
+++ b/opennlp-docs/src/docbkx/opennlp.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE book PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd">
+<!DOCTYPE book PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd">
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
 or more contributor license agreements.  See the NOTICE file
@@ -20,7 +20,7 @@ specific language governing permissions and limitations
 under the License.
 -->
 
-<book lang="en">
+<book lang="en" xmlns="http://docbook.org/ns/docbook" version="5.0">
 	<bookinfo>
 		<releaseinfo>
 			Version <?eval ${project.version}?>
@@ -31,14 +31,28 @@ under the License.
 		<subtitle>
 			<inlinemediaobject>
 				<imageobject role="fo">
-					<imagedata fileref="./images/opennlp-logo.png" />
+					<imagedata fileref="images/opennlp-logo.png" />
 				</imageobject>
 			</inlinemediaobject>
 		</subtitle>
 
+		<cover>
+			<para role="tagline">Development Manual for Apache OpenNLP</para>
+			<mediaobject>
+				<imageobject role="fo">
+					<imagedata fileref="images/opennlp-logo.png">
+						<info>
+							<othercredit>
+								<orgname>The Apache Software Foundation</orgname>
+							</othercredit>
+						</info>
+					</imagedata>
+				</imageobject>
+			</mediaobject>
+		</cover>
+
 		<authorgroup>
-			<corpauthor>Written and maintained by the Apache OpenNLP Development
-				Community</corpauthor>
+			<corpauthor>Written and maintained by the Apache OpenNLP Development Community</corpauthor>
 		</authorgroup>
 
 		<legalnotice>
@@ -55,7 +69,7 @@ under the License.
 
 					<blockquote>
 						<para>
-							<ulink url="http://www.apache.org/licenses/LICENSE-2.0" />
+							<ulink url="https://www.apache.org/licenses/LICENSE-2.0" />
 						</para>
 					</blockquote>
 
diff --git a/opennlp-docs/src/docbkx/parser.xml b/opennlp-docs/src/docbkx/parser.xml
index 468a3ea8f..f4a771d1a 100644
--- a/opennlp-docs/src/docbkx/parser.xml
+++ b/opennlp-docs/src/docbkx/parser.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -31,11 +31,15 @@ under the License.
 			A parser returns a parse tree from a sentence according to a phrase structure grammar. A parse tree specifies
 		the internal structure of a sentence. For example, the following image represents a parse tree for 
 		the sentence 'The cellphone was broken in two days':</para>
-		
-		<imagedata fileref="images/parsetree1.png" scalefit="1"/>
-		
+
+		<inlinemediaobject>
+			<imageobject role="fo">
+				<imagedata fileref="images/parsetree1.png" scalefit="1"/>
+			</imageobject>
+		</inlinemediaobject>
+
 		<para>
-			A parse tree can be used to determine the role of subtrees or constituents in the sentence. For example, it is possible to
+		A parse tree can be used to determine the role of subtrees or constituents in the sentence. For example, it is possible to
 		know that 'The cellphone' is the subject of the sentence and the verb (action) is 'was broken.'			
 		</para>
 		
@@ -47,24 +51,20 @@ under the License.
 		Download the English chunking parser model from the website and start the Parse
 		Tool with the following command.
 				<screen>
-				<![CDATA[
-$ opennlp Parser en-parser-chunking.bin]]>
+<![CDATA[$ opennlp Parser en-parser-chunking.bin]]>
 		</screen>
 		Loading the big parser model can take several seconds, be patient.
 		Copy this sample sentence to the console.
 		<screen>
-				<![CDATA[
-The cellphone was broken in two days .]]>
+<![CDATA[The cellphone was broken in two days .]]>
 		</screen>
 		The parser should now print the following to the console.
 				<screen>
-				<![CDATA[
-(TOP (S (NP (DT The) (NN cellphone)) (VP (VBD was) (VP (VBN broken) (PP (IN in) (NP (CD two) (NNS days))))) (. .)))]]>
+<![CDATA[(TOP (S (NP (DT The) (NN cellphone)) (VP (VBD was) (VP (VBN broken) (PP (IN in) (NP (CD two) (NNS days))))) (. .)))]]>
 		</screen>
 		With the following command the input can be read from a file and be written to an output file.
 				<screen>
-				<![CDATA[
-$ opennlp Parser en-parser-chunking.bin < article-tokenized.txt > article-parsed.txt.]]>
+<![CDATA[$ opennlp Parser en-parser-chunking.bin < article-tokenized.txt > article-parsed.txt.]]>
 		</screen>
 		The article-tokenized.txt file must contain one sentence per line which is
 		tokenized with the English tokenizer model from our website.
@@ -77,22 +77,18 @@ $ opennlp Parser en-parser-chunking.bin < article-tokenized.txt > article-parsed
 			The Parser can be easily integrated into an application via its API.
 			To instantiate a Parser the parser model must be loaded first.
 			<programlisting language="java">
-				<![CDATA[
-InputStream modelIn = new FileInputStream("en-parser-chunking.bin");
+<![CDATA[InputStream modelIn = new FileInputStream("en-parser-chunking.bin");
 try {
-  ParserModel model = new ParserModel(modelIn);
-}
-catch (IOException e) {
-  e.printStackTrace();
-}
-finally {
-  if (modelIn != null) {
-    try {
-      modelIn.close();
+  	ParserModel model = new ParserModel(modelIn);
+} catch (IOException e) {
+  	e.printStackTrace();
+} finally {
+  	if (modelIn != null) {
+  		  try {
+  			    modelIn.close();
     }
     catch (IOException e) {
     }
-  }
 }]]>
 		</programlisting>
 		Unlike the other components to instantiate the Parser a factory method
@@ -102,15 +98,13 @@ finally {
 		The factory method will read a type parameter from the model and create
 		an instance of the corresponding parser implementation.
 		<programlisting language="java">
-				<![CDATA[
-Parser parser = ParserFactory.create(model);]]>
+<![CDATA[Parser parser = ParserFactory.create(model);]]>
 		</programlisting>
 		Right now the tree insert parser is still experimental and there is no pre-trained model for it.
 		The parser expect a whitespace tokenized sentence. A utility method from the command
 		line tool can parse the sentence String. The following code shows how the parser can be called.
 				<programlisting language="java">
-				<![CDATA[
-String sentence = "The quick brown fox jumps over the lazy dog .";
+<![CDATA[String sentence = "The quick brown fox jumps over the lazy dog .";
 Parse[] topParses = ParserTool.parseLine(sentence, parser, 1);]]>
 		</programlisting>
 		
@@ -134,8 +128,7 @@ Parse[] topParses = ParserTool.parseLine(sentence, parser, 1);]]>
 		In the first case the training data must be available in the OpenNLP format. Which is
 		the Penn Treebank format, but with the limitation of a sentence per line.
 		<programlisting>
-				<![CDATA[
-(TOP (S (NP-SBJ (DT Some) )(VP (VBP say) (NP (NNP November) ))(. .) ))
+<![CDATA[(TOP (S (NP-SBJ (DT Some) )(VP (VBP say) (NP (NNP November) ))(. .) ))
 (TOP (S (NP-SBJ (PRP I) )(VP (VBP say) (NP (CD 1992) ))(. .) ('' '') ))]]>
 		</programlisting>
 		Penn Treebank annotation guidelines can be found on the
@@ -155,44 +148,41 @@ Parse[] topParses = ParserTool.parseLine(sentence, parser, 1);]]>
 		To train the parser a head rules file is also needed. (TODO: Add documentation about the head rules file)
 		Usage of the tool:
             <screen>
-				<![CDATA[
-$ opennlp ParserTrainer
+<![CDATA[$ opennlp ParserTrainer
 Usage: opennlp ParserTrainer -headRules headRulesFile [-parserType CHUNKING|TREEINSERT] \
                              [-params paramsFile] [-iterations num] [-cutoff num] \
                              -model modelFile -lang language -data sampleData \
                              [-encoding charsetName]
 
 Arguments description:
-	-headRules headRulesFile
-		head rules file.
-	-parserType CHUNKING|TREEINSERT
-		one of CHUNKING or TREEINSERT, default is CHUNKING.
-	-params paramsFile
-		training parameters file.
-	-iterations num
-		number of training iterations, ignored if -params is used.
-	-cutoff num
-		minimal number of times a feature must be seen, ignored if -params is used.
-	-model modelFile
-		output model file.
-	-format formatName
-		data format, might have its own parameters.
-	-encoding charsetName
-		encoding for reading and writing text, if absent the system default is used.
-	-lang language
-		language which is being processed.
-	-data sampleData
-		data to be used, usually a file name.
-	-encoding charsetName
-		encoding for reading and writing text, if absent the system default is used.]]>
+		-headRules headRulesFile
+				head rules file.
+		-parserType CHUNKING|TREEINSERT
+				one of CHUNKING or TREEINSERT, default is CHUNKING.
+		-params paramsFile
+				training parameters file.
+		-iterations num
+				number of training iterations, ignored if -params is used.
+		-cutoff num
+				minimal number of times a feature must be seen, ignored if -params is used.
+		-model modelFile
+				output model file.
+		-format formatName
+				data format, might have its own parameters.
+		-encoding charsetName
+				encoding for reading and writing text, if absent the system default is used.
+		-lang language
+				language which is being processed.
+		-data sampleData
+				data to be used, usually a file name.
+		-encoding charsetName
+				encoding for reading and writing text, if absent the system default is used.]]>
         </screen>
 		The model on the website was trained with the following command:
 		<screen>
-		<![CDATA[
-$ opennlp ParserTrainer -model en-parser-chunking.bin -parserType CHUNKING \
+<![CDATA[$ opennlp ParserTrainer -model en-parser-chunking.bin -parserType CHUNKING \
                         -headRules head_rules \
-                        -lang en -data train.all -encoding ISO-8859-1
-    ]]>
+                        -lang en -data train.all -encoding ISO-8859-1]]>
 		</screen>
 		Its also possible to specify the cutoff and the number of iterations, these parameters
 		are used for all trained models. The -parserType parameter is an optional parameter,
@@ -203,8 +193,7 @@ $ opennlp ParserTrainer -model en-parser-chunking.bin -parserType CHUNKING \
 		<emphasis role="strong">Note</emphasis>: The original parser model will be overwritten with the new parser model which
 		contains the replaced tagger model.
         <screen>
-		<![CDATA[
-$ opennlp TaggerModelReplacer en-parser-chunking.bin en-pos-maxent.bin]]>
+<![CDATA[$ opennlp TaggerModelReplacer en-parser-chunking.bin en-pos-maxent.bin]]>
 		</screen>
 		Additionally there are tools to just retrain the build or the check model.
 		</para>
@@ -231,76 +220,62 @@ $ opennlp TaggerModelReplacer en-parser-chunking.bin en-pos-maxent.bin]]>
 		</itemizedlist>
 		The following code snippet shows how to instantiate the HeadRules:
 		<programlisting language="java">
-				<![CDATA[
-static HeadRules createHeadRules(TrainerToolParams params) throws IOException {
-
+<![CDATA[static HeadRules createHeadRules(TrainerToolParams params) throws IOException {
     ArtifactSerializer headRulesSerializer = null;
-
     if (params.getHeadRulesSerializerImpl() != null) {
-      headRulesSerializer = ExtensionLoader.instantiateExtension(ArtifactSerializer.class,
+      	headRulesSerializer = ExtensionLoader.instantiateExtension(ArtifactSerializer.class,
               params.getHeadRulesSerializerImpl());
-    }
-    else {
+    } else {
       if ("eng".equals(params.getLang())) {
-        headRulesSerializer = new opennlp.tools.parser.lang.en.HeadRules.HeadRulesSerializer();
-      }
-      else if ("es".equals(params.getLang())) {
-        headRulesSerializer = new opennlp.tools.parser.lang.es.AncoraSpanishHeadRules.HeadRulesSerializer();
-      }
-      else {
-        // default for now, this case should probably cause an error ...
-        headRulesSerializer = new opennlp.tools.parser.lang.en.HeadRules.HeadRulesSerializer();
+        	headRulesSerializer = new opennlp.tools.parser.lang.en.HeadRules.HeadRulesSerializer();
+      } else if ("es".equals(params.getLang())) {
+        	headRulesSerializer = new opennlp.tools.parser.lang.es.AncoraSpanishHeadRules.HeadRulesSerializer();
+      } else {
+        	// default for now, this case should probably cause an error ...
+        	headRulesSerializer = new opennlp.tools.parser.lang.en.HeadRules.HeadRulesSerializer();
       }
     }
 
     Object headRulesObject = headRulesSerializer.create(new FileInputStream(params.getHeadRules()));
-
     if (headRulesObject instanceof HeadRules) {
-      return (HeadRules) headRulesObject;
-    }
-    else {
-      throw new TerminateToolException(-1, "HeadRules Artifact Serializer must create an object of type HeadRules!");
+      	return (HeadRules) headRulesObject;
+    } else {
+      	throw new TerminateToolException(-1, "HeadRules Artifact Serializer must create an object of type HeadRules!");
     }
 }]]>
 	</programlisting>
 		The following code illustrates the three other steps, namely, opening the data, training
 	the model and saving the ParserModel into an output file.
 		<programlisting language="java">
-				<![CDATA[
-ParserModel model = null;
+<![CDATA[ParserModel model = null;
 File modelOutFile = params.getModel();
 CmdLineUtil.checkOutputFile("parser model", modelOutFile);
 
 try {
-  HeadRules rules = createHeadRules(params);
-  InputStreamFactory inputStreamFactory = new MarkableFileInputStreamFactory(new File("parsing.train"));
-  ObjectStream<String> stringStream = new PlainTextByLineStream(inputStreamFactory, StandardCharsets.UTF_8);
-  ObjectStream<Parse> sampleStream = new ParseSample(stringStream);
-
-  ParserType type = parseParserType(params.getParserType());
-  if (ParserType.CHUNKING.equals(type)) {
-    model = opennlp.tools.parser.chunking.Parser.train(
-            params.getLang(), sampleStream, rules,
-            mlParams);
-  } else if (ParserType.TREEINSERT.equals(type)) {
-    model = opennlp.tools.parser.treeinsert.Parser.train(params.getLang(), sampleStream, rules,
-            mlParams);
-    }
-  }
-  catch (IOException e) {
-      throw new TerminateToolException(-1, "IO error while reading training data or indexing data: "
-          + e.getMessage(), e);
-  }
-  finally {
-      try {
-        sampleStream.close();
-      }
-      catch (IOException e) {
-        // sorry that this can fail
-      }
-  }
-  CmdLineUtil.writeModel("parser", modelOutFile, model);
-]]>
+		HeadRules rules = createHeadRules(params);
+		InputStreamFactory inputStreamFactory = new MarkableFileInputStreamFactory(new File("parsing.train"));
+		ObjectStream<String> stringStream = new PlainTextByLineStream(inputStreamFactory, StandardCharsets.UTF_8);
+		ObjectStream<Parse> sampleStream = new ParseSample(stringStream);
+		ParserType type = parseParserType(params.getParserType());
+		if (ParserType.CHUNKING.equals(type)) {
+			model = opennlp.tools.parser.chunking.Parser.train(
+							params.getLang(), sampleStream, rules,
+							mlParams);
+		} else if (ParserType.TREEINSERT.equals(type)) {
+			model = opennlp.tools.parser.treeinsert.Parser.train(params.getLang(), sampleStream, rules,
+							mlParams);
+			}
+		} catch (IOException e) {
+				throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.getMessage(), e);
+		} finally {
+				try {
+					sampleStream.close();
+				}
+				catch (IOException e) {
+					// sorry that this can fail
+				}
+		}
+		CmdLineUtil.writeModel("parser", modelOutFile, model);]]>
 	</programlisting>
 		</para>
 		</section>
@@ -317,8 +292,7 @@ try {
 			<para>
 				The following command shows how the tool can be run:
 				<screen>
-				<![CDATA[
-$ opennlp ParserEvaluator
+<![CDATA[$ opennlp ParserEvaluator
 Usage: opennlp ParserEvaluator[.ontonotes|frenchtreebank] [-misclassified true|false] -model model \
                -data sampleData [-encoding charsetName]]]>
 		</screen>
@@ -326,13 +300,11 @@ Usage: opennlp ParserEvaluator[.ontonotes|frenchtreebank] [-misclassified true|f
 				en-parser-chunking.eval,
 				and you trained a model called en-parser-chunking.bin:
 				<screen>
-				<![CDATA[
-$ opennlp ParserEvaluator -model en-parser-chunking.bin -data en-parser-chunking.eval -encoding UTF-8]]>
+<![CDATA[$ opennlp ParserEvaluator -model en-parser-chunking.bin -data en-parser-chunking.eval -encoding UTF-8]]>
 		</screen>
 				and here is a sample output:
 				<screen>
-		<![CDATA[
-Precision: 0.9009744742967609
+<![CDATA[Precision: 0.9009744742967609
 Recall: 0.8962012400910446
 F-Measure: 0.8985815184245214]]>
 		</screen>
@@ -359,21 +331,18 @@ F-Measure: 0.8985815184245214]]>
 		In the first case the model must be loaded and a Parse ObjectStream must be created (see code samples above),
 		assuming these two objects exist the following code shows how to perform the evaluation:
 			<programlisting language="java">
-			  <![CDATA[
-Parser parser = ParserFactory.create(model);
+<![CDATA[Parser parser = ParserFactory.create(model);
 ParserEvaluator evaluator = new ParserEvaluator(parser);
 evaluator.evaluate(sampleStream);
 
 FMeasure result = evaluator.getFMeasure();
-
 System.out.println(result.toString());]]>
 			</programlisting>
 			In the cross validation case all the training arguments must be
 			provided (see the Training API section above).
 			To perform cross validation the ObjectStream must be resettable.
 			<programlisting language="java">
-				<![CDATA[
-InputStreamFactory inputStreamFactory = new MarkableFileInputStreamFactory(new File("parsing.train"));
+<![CDATA[InputStreamFactory inputStreamFactory = new MarkableFileInputStreamFactory(new File("parsing.train"));
 ObjectStream<String> stringStream = new PlainTextByLineStream(inputStreamFactory, StandardCharsets.UTF_8);
 ObjectStream<Parse> sampleStream = new ParseSample(stringStream);
 ParserCrossValidator evaluator = new ParserCrossValidator("eng", trainParameters, headRules, \
@@ -381,7 +350,6 @@ parserType, listeners.toArray(new ParserEvaluationMonitor[listeners.size()])));
 evaluator.evaluate(sampleStream, 10);
 
 FMeasure result = evaluator.getFMeasure();
-
 System.out.println(result.toString());]]>
 			</programlisting>
 		</para>
diff --git a/opennlp-docs/src/docbkx/postagger.xml b/opennlp-docs/src/docbkx/postagger.xml
index 38f4609a9..103a4e354 100644
--- a/opennlp-docs/src/docbkx/postagger.xml
+++ b/opennlp-docs/src/docbkx/postagger.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -40,20 +40,17 @@ under the License.
 		only intended for demonstration and testing.
 		Download the English maxent pos model and start the POS Tagger Tool with this command:
 		<screen>
-			<![CDATA[
-$ opennlp POSTagger opennlp-en-ud-ewt-pos-1.3-2.5.4.bin]]>
+<![CDATA[$ opennlp POSTagger opennlp-en-ud-ewt-pos-1.3-2.5.4.bin]]>
 		 </screen>
 		The POS Tagger now reads a tokenized sentence per line from stdin.
 		Copy these two sentences to the console:
 		<screen>
-			<![CDATA[
-Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
+<![CDATA[Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
 Mr. Vinken is chairman of Elsevier N.V. , the Dutch publishing group .]]>
 		 </screen>
 		 The POS Tagger will now echo the sentences with pos tags to the console:
 		<screen>
-			<![CDATA[
-Pierre_PROPN Vinken_PROPN ,_PUNCT 61_NUM years_NOUN old_ADJ ,_PUNCT will_AUX join_VERB the_DET board_NOUN as_ADP
+<![CDATA[Pierre_PROPN Vinken_PROPN ,_PUNCT 61_NUM years_NOUN old_ADJ ,_PUNCT will_AUX join_VERB the_DET board_NOUN as_ADP
 		a_DET nonexecutive_ADJ director_NOUN Nov._PROPN 29_NUM ._PUNCT
 Mr._PROPN Vinken_PROPN is_AUX chairman_NOUN of_ADP Elsevier_ADJ N.V._PROPN ,_PUNCT the_DET Dutch_PROPN publishing_VERB group_NOUN .]]>
 		 </screen>
@@ -68,15 +65,13 @@ Mr._PROPN Vinken_PROPN is_AUX chairman_NOUN of_ADP Elsevier_ADJ N.V._PROPN ,_PUN
 			First the pos model must be loaded into memory from disk or another source.
 			In the sample below it is loaded from disk.
 			<programlisting language="java">
-				<![CDATA[
-try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-pos-1.3-2.5.4.bin"){
+<![CDATA[try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-pos-1.3-2.5.4.bin"){
   POSModel model = new POSModel(modelIn);
 }]]>
 			</programlisting>
 			After the model is loaded the POSTaggerME can be instantiated.
 			<programlisting language="java">
-				<![CDATA[
-POSTaggerME tagger = new POSTaggerME(model);]]>
+<![CDATA[POSTaggerME tagger = new POSTaggerME(model);]]>
 			</programlisting>
 			The POS Tagger instance is now ready to tag data. It expects a tokenized sentence
 			as input, which is represented as a String array, each String object in the array
@@ -85,8 +80,7 @@ POSTaggerME tagger = new POSTaggerME(model);]]>
 	   <para>
 	   The following code shows how to determine the most likely pos tag sequence for a sentence.
 	   	<programlisting language="java">
-		  <![CDATA[
-String[] sent = new String[]{"Most", "large", "cities", "in", "the", "US", "had",
+<![CDATA[String[] sent = new String[]{"Most", "large", "cities", "in", "the", "US", "had",
                              "morning", "and", "afternoon", "newspapers", "."};		  
 String[] tags = tagger.tag(sent);]]>
 			</programlisting>
@@ -95,8 +89,7 @@ String[] tags = tagger.tag(sent);]]>
 			The confidence scores for the returned tags can be easily retrieved from
 			a POSTaggerME with the following method call:
 				   	<programlisting language="java">
-		  <![CDATA[
-double[] probs = tagger.probs();]]>
+<![CDATA[double[] probs = tagger.probs();]]>
 			</programlisting>
 			The call to probs is stateful and will always return the probabilities of the last
 			tagged sentence. The probs method should only be called when the tag method
@@ -108,8 +101,7 @@ double[] probs = tagger.probs();]]>
 			The topKSequences method is capable of returning the top sequences.
 			It can be called in a similar way as tag.
 			<programlisting language="java">
-		  <![CDATA[
-Sequence[] topSequences = tagger.topKSequences(sent);]]>
+<![CDATA[Sequence[] topSequences = tagger.topKSequences(sent);]]>
 			</programlisting>	
 			Each Sequence object contains one sequence. The sequence can be retrieved
 			via Sequence.getOutcomes() which returns a tags array 
@@ -124,8 +116,7 @@ Sequence[] topSequences = tagger.topKSequences(sent);]]>
 			is a collection of tokenized sentences where each token has the assigned part-of-speech tag.
 			The native POS Tagger training material looks like this:
 			<screen>
-		  <![CDATA[
-About_ADV 10_NUM Euro_PROPN ,_PUNCT I_PRON reckon._PUNCT
+<![CDATA[About_ADV 10_NUM Euro_PROPN ,_PUNCT I_PRON reckon._PUNCT
 That_PRON sounds_VERB good_ADJ ._PUNCT]]>
 			</screen>
 			Each sentence must be in one line. The token/tag pairs are combined with "_".
@@ -146,8 +137,7 @@ That_PRON sounds_VERB good_ADJ ._PUNCT]]>
 		<para>
 		    Usage of the tool:
             <screen>
-				<![CDATA[
-$ opennlp POSTaggerTrainer
+<![CDATA[$ opennlp POSTaggerTrainer
 Usage: opennlp POSTaggerTrainer[.conllx] [-type maxent|perceptron|perceptron_sequence] \
                [-dict dictionaryPath] [-ngram cutoff] [-params paramsFile] [-iterations num] \
                [-cutoff num] -model modelFile -lang language -data sampleData \
@@ -179,8 +169,7 @@ Arguments description:
 		<para>
 		    The following command illustrates how an English part-of-speech model can be trained:
 		    <screen>
-		  <![CDATA[
-$ opennlp POSTaggerTrainer -type maxent -model en-custom-pos-maxent.bin \
+<![CDATA[$ opennlp POSTaggerTrainer -type maxent -model en-custom-pos-maxent.bin \
                            -lang en -data en-custom-pos.train -encoding UTF-8]]>
 		    </screen>
 		</para>
@@ -203,8 +192,7 @@ $ opennlp POSTaggerTrainer -type maxent -model en-custom-pos-maxent.bin \
 		</itemizedlist>
 		The following code illustrates that:
 		<programlisting language="java">
-				<![CDATA[
-POSModel model = null;
+<![CDATA[POSModel model = null;
 
 try {
   ObjectStream<String> lineStream = new PlainTextByLineStream(
@@ -221,8 +209,7 @@ try {
 	the model. The trained model must still be saved into an OutputStream, in
 	the sample below it is written into a file.
 	<programlisting language="java">
-				<![CDATA[
-try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(modelFile))){
+<![CDATA[try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(modelFile))){
   model.serialize(modelOut);
 }]]>
 		</programlisting>
@@ -242,16 +229,14 @@ try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(model
 		<para>
 		Sample POS Training material (file : en-custom-pos.train)
 			<screen>
-				<![CDATA[
-It_PRON is_OTHER spring_PROPN season_NOUN. The_DET flowers_NOUN are_OTHER red_ADJ and_CCONJ yellow_ADJ ._PUNCT
+<![CDATA[It_PRON is_OTHER spring_PROPN season_NOUN. The_DET flowers_NOUN are_OTHER red_ADJ and_CCONJ yellow_ADJ ._PUNCT
 Red_NOUN is_OTHER my_DET favourite_ADJ colour_NOUN ._PUNCT]]>
 			</screen>
 		</para>
 		<para>
 		Sample Tag Dictionary (file : dictionary.xml)
 			<programlisting language="xml">
-				<![CDATA[
-<?xml version="1.0" encoding="UTF-8"?>
+<![CDATA[<?xml version="1.0" encoding="UTF-8"?>
  <dictionary case_sensitive="false">
   <entry tags="PRON">
     <token>It</token>
@@ -300,8 +285,7 @@ Red_NOUN is_OTHER my_DET favourite_ADJ colour_NOUN ._PUNCT]]>
 		</para>
 		<para>Sample code to train a model using above tag dictionary
 			<programlisting language="java">
-			<![CDATA[
-POSModel model = null;
+<![CDATA[POSModel model = null;
 	try {
 		ObjectStream<String> lineStream = new PlainTextByLineStream(
 				new MarkableFileInputStreamFactory(new File("en-custom-pos.train")), StandardCharsets.UTF_8);
@@ -328,8 +312,7 @@ POSModel model = null;
 		<para>
 		The custom model is then used to tag a sequence.
 		<programlisting language="java">
-			<![CDATA[
-String[] sent = new String[]{"Spring", "is", "my", "favourite", "season", "."};
+<![CDATA[String[] sent = new String[]{"Spring", "is", "my", "favourite", "season", "."};
 String[] tags = tagger.tag(sent);
 Arrays.stream(tags).forEach(k -> System.out.print(k + " "));]]>
 		</programlisting>
@@ -340,10 +323,12 @@ Arrays.stream(tags).forEach(k -> System.out.print(k + " "));]]>
 				    Sentence:	Spring is my favourite season.
 
 				Output
-				    POS Tags using the custom model (en-custom-pos-maxent.bin): PROPN OTHER PRON ADJ NOUN PUNCT
+				    POS Tags using the custom model (en-custom-pos-maxent.bin): \
+							PROPN OTHER PRON ADJ NOUN PUNCT
 
 				Output with the default model
-				    POS Tags using the default model (opennlp-en-ud-ewt-pos-1.3-2.5.4.bin):	NOUN AUX PRON ADJ NOUN PUNCT
+				    POS Tags using the default model (opennlp-en-ud-ewt-pos-1.3-2.5.4.bin):	\
+							NOUN AUX PRON ADJ NOUN PUNCT
 			</literallayout>
 		</para>
 		</section>
@@ -361,13 +346,11 @@ Arrays.stream(tags).forEach(k -> System.out.print(k + " "));]]>
 		There is a command line tool to evaluate a given model on a test data set.
 		The following command shows how the tool can be run:
 		<screen>
-				<![CDATA[
-$ opennlp POSTaggerEvaluator -model pt.postagger.bin -data pt.postagger.test -encoding utf-8]]>
+<![CDATA[$ opennlp POSTaggerEvaluator -model pt.postagger.bin -data pt.postagger.test -encoding utf-8]]>
 			 </screen>
 			 This will display the resulting accuracy score, e.g.:
 			 <screen>
-				<![CDATA[
-Loading model ... done
+<![CDATA[Loading model ... done
 Evaluating ... done
 
 Accuracy: 0.9659110277825124]]>
@@ -377,13 +360,11 @@ Accuracy: 0.9659110277825124]]>
             There is a command line tool for cross-validation of the test data set.
             The following command shows how the tool can be run:
             <screen>
-                    <![CDATA[
-$ opennlp POSTaggerCrossValidator -lang pt -data pt.postagger.test -encoding utf-8]]>
+<![CDATA[$ opennlp POSTaggerCrossValidator -lang pt -data pt.postagger.test -encoding utf-8]]>
                  </screen>
                  This will display the resulting accuracy score, e.g.:
                  <screen>
-                    <![CDATA[
-Accuracy: 0.9659110277825124]]>
+<![CDATA[Accuracy: 0.9659110277825124]]>
                  </screen>
             </para>
 
diff --git a/opennlp-docs/src/docbkx/sentdetect.xml b/opennlp-docs/src/docbkx/sentdetect.xml
index 8aea2b0ff..75ebc1244 100644
--- a/opennlp-docs/src/docbkx/sentdetect.xml
+++ b/opennlp-docs/src/docbkx/sentdetect.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -36,16 +36,14 @@ under the License.
 		last non whitespace character is assumed to be a sentence end.
 		The sample text below should be segmented into its sentences.
 		<screen>
-				<![CDATA[
-Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29. Mr. Vinken is
+<![CDATA[Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29. Mr. Vinken is
 chairman of Elsevier N.V., the Dutch publishing group. Rudolph Agnew, 55 years
 old and former chairman of Consolidated Gold Fields PLC, was named a director of this
 British industrial conglomerate.]]>
 		</screen>
 		After detecting the sentence boundaries each sentence is written in its own line.
 		<screen>
-				<![CDATA[
-Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.
+<![CDATA[Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.
 Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group.
 Rudolph Agnew, 55 years old and former chairman of Consolidated Gold Fields PLC,
     was named a director of this British industrial conglomerate.]]>
@@ -62,14 +60,12 @@ Rudolph Agnew, 55 years old and former chairman of Consolidated Gold Fields PLC,
 		The easiest way to try out the Sentence Detector is the command line tool. The tool is only intended for demonstration and testing.
 		Download the english sentence detector model and start the Sentence Detector Tool with this command:
         <screen>
-        <![CDATA[
-$ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin]]>
+<![CDATA[$ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin]]>
 		</screen>
 		Just copy the sample text from above to the console. The Sentence Detector will read it and echo one sentence per line to the console.
 		Usually the input is read from a file and the output is redirected to another file. This can be achieved with the following command.
 		<screen>
-				<![CDATA[
-$ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin < input.txt > output.txt]]>
+<![CDATA[$ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin < input.txt > output.txt]]>
 		</screen>
 		For the english sentence model from the website the input text should not be tokenized.
 		</para>
@@ -80,30 +76,26 @@ $ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin < input.txt
 		The Sentence Detector can be easily integrated into an application via its API.
 		To instantiate the Sentence Detector the sentence model must be loaded first.
 		<programlisting language="java">
-				<![CDATA[
-try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin")) {
+<![CDATA[try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin")) {
   SentenceModel model = new SentenceModel(modelIn);
 }]]>
 		</programlisting>
 		After the model is loaded the SentenceDetectorME can be instantiated.
 		<programlisting language="java">
-				<![CDATA[
-SentenceDetectorME sentenceDetector = new SentenceDetectorME(model);]]>
+<![CDATA[SentenceDetectorME sentenceDetector = new SentenceDetectorME(model);]]>
 		</programlisting>
 		The Sentence Detector can output an array of Strings, where each String is one sentence.
 				<programlisting language="java">
-				<![CDATA[
-String[] sentences = sentenceDetector.sentDetect("  First sentence. Second sentence. ");]]>
+<![CDATA[String[] sentences = sentenceDetector.sentDetect("  First sentence. Second sentence. ");]]>
 		</programlisting>
 		The result array now contains two entries. The first String is "First sentence." and the
         second String is "Second sentence." The whitespace before, between and after the input String is removed.
 		The API also offers a method which simply returns the span of the sentence in the input string.
 		<programlisting language="java">
-				<![CDATA[
-Span[] sentences = sentenceDetector.sentPosDetect("  First sentence. Second sentence. ");]]>
+<![CDATA[Span[] sentences = sentenceDetector.sentPosDetect("  First sentence. Second sentence. ");]]>
 		</programlisting>
-		The result array again contains two entries. The first span beings at index 2 and ends at
-            17. The second span begins at 18 and ends at 34. The utility method Span.getCoveredText can be used to create a substring which only covers the chars in the span.
+		The result array again contains two entries. The first span beings at index 2 and ends at 17.
+		The second span begins at 18 and ends at 34. The utility method Span.getCoveredText can be used to create a substring which only covers the chars in the span.
 		</para>
 		</section>
 	</section>
@@ -113,50 +105,46 @@ Span[] sentences = sentenceDetector.sentPosDetect("  First sentence. Second sent
 		<section id="tools.sentdetect.training.tool">
 		<title>Training Tool</title>
 		<para>
-		OpenNLP has a command line tool which is used to train the models available from the model
-		download page on various corpora. The data must be converted to the OpenNLP Sentence Detector
-		training format. Which is one sentence per line. An empty line indicates a document boundary.
-		In case the document boundary is unknown, it's recommended to have an empty line every few ten
-		sentences. Exactly like the output in the sample above.
-		Usage of the tool:
-		<screen>
-				<![CDATA[
-$ opennlp SentenceDetectorTrainer
+			OpenNLP has a command line tool which is used to train the models available from the model
+			download page on various corpora. The data must be converted to the OpenNLP Sentence Detector
+			training format. Which is one sentence per line. An empty line indicates a document boundary.
+			In case the document boundary is unknown, it's recommended to have an empty line every few ten
+			sentences. Exactly like the output in the sample above.
+			Usage of the tool:
+			<screen>
+<![CDATA[$ opennlp SentenceDetectorTrainer
 Usage: opennlp SentenceDetectorTrainer[.namefinder|.conllx|.pos] [-abbDict path] \
-               [-params paramsFile] [-iterations num] [-cutoff num] -model modelFile \
-               -lang language -data sampleData [-encoding charsetName]
+								 [-params paramsFile] [-iterations num] [-cutoff num] -model modelFile \
+								 -lang language -data sampleData [-encoding charsetName]
 
 Arguments description:
-        -abbDict path
-                abbreviation dictionary in XML format.
-        -params paramsFile
-                training parameters file.
-        -iterations num
-                number of training iterations, ignored if -params is used.
-        -cutoff num
-                minimal number of times a feature must be seen, ignored if -params is used.
-        -model modelFile
-                output model file.
-        -lang language
-                language which is being processed.
-        -data sampleData
-                data to be used, usually a file name.
-        -encoding charsetName
-                encoding for reading and writing text, if absent the system default is used.
-        -useTokenEnd boolean flag
-                set to false when the next sentence in the test dataset doesn't start with a blank space post completion of
-                the previous sentence. If absent, it is defaulted to true.]]>
-	</screen>
+					-abbDict path
+									abbreviation dictionary in XML format.
+					-params paramsFile
+									training parameters file.
+					-iterations num
+									number of training iterations, ignored if -params is used.
+					-cutoff num
+									minimal number of times a feature must be seen, ignored if -params is used.
+					-model modelFile
+									output model file.
+					-lang language
+									language which is being processed.
+					-data sampleData
+									data to be used, usually a file name.
+					-encoding charsetName
+									encoding for reading and writing text, if absent the system default is used.
+					-useTokenEnd boolean flag
+									set to false when the next sentence in the test dataset doesn't start with a blank space post completion of
+									the previous sentence. If absent, it is defaulted to true.]]>
+		</screen>
 		To train an English sentence detector use the following command:
         <screen>
-				<![CDATA[
-$ opennlp SentenceDetectorTrainer -model en-custom-sent.bin -lang en -data en-custom-sent.train -encoding UTF-8
-                        ]]>
+<![CDATA[$ opennlp SentenceDetectorTrainer -model en-custom-sent.bin -lang en -data en-custom-sent.train -encoding UTF-8]]>
         </screen>
-            It should produce the following output:
-            <screen>
-                <![CDATA[
-Indexing events using cutoff of 5
+		It should produce the following output:
+				<screen>
+<![CDATA[Indexing events using cutoff of 5
 
 	Computing event counts...  done. 4883 events
 	Indexing...  done.
@@ -208,13 +196,10 @@ Path: en-custom-sent.bin
 			</itemizedlist>
 			The following sample code illustrates these steps:
 					<programlisting language="java">
-				<![CDATA[
-
-ObjectStream<String> lineStream =
+<![CDATA[ObjectStream<String> lineStream =
   new PlainTextByLineStream(new MarkableFileInputStreamFactory(new File("en-custom-sent.train")), StandardCharsets.UTF_8);
 
 SentenceModel model;
-
 try (ObjectStream<SentenceSample> sampleStream = new SentenceSampleStream(lineStream)) {
   model = SentenceDetectorME.train("eng", sampleStream,
       new SentenceDetectorFactory("eng", true, null, null), TrainingParameters.defaultParams());
@@ -234,10 +219,9 @@ try (OutputStream modelOut = new BufferedOutputStream(new FileOutputStream(model
 		<section id="tools.sentdetect.eval.tool">
 			<title>Evaluation Tool</title>
 			<para>
-                The command shows how the evaluator tool can be run:
-                <screen>
-				<![CDATA[
-$ opennlp SentenceDetectorEvaluator -model en-custom-sent.bin -data en-custom-sent.eval -encoding UTF-8
+				The command shows how the evaluator tool can be run:
+				<screen>
+<![CDATA[$ opennlp SentenceDetectorEvaluator -model en-custom-sent.bin -data en-custom-sent.eval -encoding UTF-8
 
 Loading model ... done
 Evaluating ... done
@@ -245,8 +229,8 @@ Evaluating ... done
 Precision: 0.9465737514518002
 Recall: 0.9095982142857143
 F-Measure: 0.9277177006260672]]>
-                </screen>
-                The en-custom-sent.eval file has the same format as the training data.
+				</screen>
+			The en-custom-sent.eval file has the same format as the training data.
 			</para>
 		</section>
 	</section>
diff --git a/opennlp-docs/src/docbkx/tokenizer.xml b/opennlp-docs/src/docbkx/tokenizer.xml
index 1582ef1a0..d615b761b 100644
--- a/opennlp-docs/src/docbkx/tokenizer.xml
+++ b/opennlp-docs/src/docbkx/tokenizer.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor 
 	license agreements. See the NOTICE file distributed with this work for additional 
@@ -25,27 +25,23 @@
 			words, punctuation, numbers, etc.
 
 			<screen>
-			<![CDATA[
-Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.
+<![CDATA[Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29.
 Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group.
 Rudolph Agnew, 55 years old and former chairman of Consolidated Gold Fields
-    PLC, was named a director of this British industrial conglomerate.
-			]]>
+	PLC, was named a director of this British industrial conglomerate.]]>
 		    </screen>
 
 			The following result shows the individual tokens in a whitespace
 			separated representation.
 
 			<screen>
-			<![CDATA[
-Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
+<![CDATA[Pierre Vinken , 61 years old , will join the board as a nonexecutive director Nov. 29 .
 Mr. Vinken is chairman of Elsevier N.V. , the Dutch publishing group .
 Rudolph Agnew , 55 years old and former chairman of Consolidated Gold Fields PLC ,
-    was named a nonexecutive director of this British industrial conglomerate . 
+	was named a nonexecutive director of this British industrial conglomerate .
 A form of asbestos once used to make Kent cigarette filters has caused a high
-    percentage of cancer deaths among a group of workers exposed to it more than 30 years ago ,
-    researchers reported . 
-			]]>
+	percentage of cancer deaths among a group of workers exposed to it more than 30 years ago ,
+	researchers reported .]]>
 		 	</screen>
 
 			OpenNLP offers multiple tokenizer implementations:
@@ -90,14 +86,12 @@ A form of asbestos once used to make Kent cigarette filters has caused a high
 			The following command shows how to use the Simple Tokenizer Tool.
 
 			<screen>
-			<![CDATA[
-$ opennlp SimpleTokenizer]]>
+<![CDATA[$ opennlp SimpleTokenizer]]>
 		    </screen>
 			To use the learnable tokenizer download the english token model from
 			our website.
 			<screen>
-			<![CDATA[
-$ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin]]>
+<![CDATA[$ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin]]>
 		    </screen>
 			To test the tokenizer copy the sample from above to the console. The
 			whitespace separated tokens will be written back to the
@@ -106,8 +100,7 @@ $ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin]]>
 		<para>
 			Usually the input is read from a file and written to a file.
 			<screen>
-			<![CDATA[
-$ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin < article.txt > article-tokenized.txt]]>
+<![CDATA[$ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin < article.txt > article-tokenized.txt]]>
 		    </screen>
 			It can be done in the same way for the Simple Tokenizer.
 		</para>
@@ -117,8 +110,7 @@ $ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin < article.txt > art
 			boundary detection and tokenization. The following sample illustrates
 			that.
 			<screen>
-			<![CDATA[
-$ opennlp SentenceDetector sentdetect.model < article.txt | opennlp TokenizerME tokenize.model | more
+<![CDATA[$ opennlp SentenceDetector sentdetect.model < article.txt | opennlp TokenizerME tokenize.model | more
 Loading model ... Loading model ... done
 done
 Showa Shell gained 20 to 1,570 and Mitsubishi Oil rose 50 to 1,500.
@@ -150,15 +142,13 @@ London share prices were bolstered largely by continued gains on Wall Street and
 			must be created first. The following code sample shows how a model
 			can be loaded.
 			<programlisting language="java">
-			<![CDATA[
-try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin")) {
+<![CDATA[try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin")) {
   TokenizerModel model = new TokenizerModel(modelIn);
 }]]>
 		 </programlisting>
 			After the model is loaded the TokenizerME can be instantiated.
 			<programlisting language="java">
-			<![CDATA[
-Tokenizer tokenizer = new TokenizerME(model);]]>
+<![CDATA[Tokenizer tokenizer = new TokenizerME(model);]]>
 		 </programlisting>
 			The tokenizer offers two tokenize methods, both expect an input
 			String object which contains the untokenized text. If possible it
@@ -166,20 +156,17 @@ Tokenizer tokenizer = new TokenizerME(model);]]>
 			tokenizer this is not required. The first returns an array of
 			Strings, where each String is one token.
 			<programlisting language="java">
-			<![CDATA[
-String[] tokens = tokenizer.tokenize("An input sample sentence.");]]>
+<![CDATA[String[] tokens = tokenizer.tokenize("An input sample sentence.");]]>
 		 </programlisting>
 			The output will be an array with these tokens.
 			<programlisting>
-			<![CDATA[
-"An", "input", "sample", "sentence", "."]]>
+<![CDATA["An", "input", "sample", "sentence", "."]]>
 		 </programlisting>
 			The second method, tokenizePos returns an array of Spans, each Span
 			contain the start and end character offsets of the token in the input
 			String.
 			<programlisting language="java">
-			<![CDATA[
-Span[] tokenSpans = tokenizer.tokenizePos("An input sample sentence.");]]>		
+<![CDATA[Span[] tokenSpans = tokenizer.tokenizePos("An input sample sentence.");]]>
 			</programlisting>
 			The tokenSpans array now contain 5 elements. To get the text for one
 			span call Span.getCoveredText which takes a span and the input text.
@@ -188,9 +175,8 @@ Span[] tokenSpans = tokenizer.tokenizePos("An input sample sentence.");]]>
 			tokens. The getTokenProbabilities method must be called directly
 			after one of the tokenize methods was called.
 			<programlisting language="java">
-			<![CDATA[
+<![CDATA[
 TokenizerME tokenizer = ...
-
 String[] tokens = tokenizer.tokenize(...);
 double[] tokenProbs = tokenizer.getTokenProbabilities();]]>
 			</programlisting>
@@ -210,22 +196,20 @@ double[] tokenProbs = tokenizer.getTokenProbabilities();]]>
 				OpenNLP has a command line tool which is used to train the models
 				available from the model download page on various corpora. The data
 				can be converted to the OpenNLP Tokenizer training format or used directly.
-                The OpenNLP format contains one sentence per line. Tokens are either separated by a
-                whitespace or by a special &lt;SPLIT&gt; tag. Tokens are split automatically on whitespace
-                and at least one &lt;SPLIT&gt; tag must be present in the training text.
+				The OpenNLP format contains one sentence per line. Tokens are either separated by a
+				whitespace or by a special &lt;SPLIT&gt; tag. Tokens are split automatically on whitespace
+				and at least one &lt;SPLIT&gt; tag must be present in the training text.
 				
 				The following sample shows the sample from above in the correct format.
 				<screen>
-			    <![CDATA[
-Pierre Vinken<SPLIT>, 61 years old<SPLIT>, will join the board as a nonexecutive director Nov. 29<SPLIT>.
+<![CDATA[Pierre Vinken<SPLIT>, 61 years old<SPLIT>, will join the board as a nonexecutive director Nov. 29<SPLIT>.
 Mr. Vinken is chairman of Elsevier N.V.<SPLIT>, the Dutch publishing group<SPLIT>.
 Rudolph Agnew<SPLIT>, 55 years old and former chairman of Consolidated Gold Fields PLC<SPLIT>,
     was named a nonexecutive director of this British industrial conglomerate<SPLIT>.]]>
 			    </screen>
 			    Usage of the tool:
 			    <screen>
-			    <![CDATA[
-$ opennlp TokenizerTrainer
+<![CDATA[$ opennlp TokenizerTrainer
 Usage: opennlp TokenizerTrainer[.namefinder|.conllx|.pos] [-abbDict path] \
                 [-alphaNumOpt isAlphaNumOpt] [-params paramsFile] [-iterations num] \
                 [-cutoff num] -model modelFile -lang language -data sampleData \
@@ -253,8 +237,7 @@ Arguments description:
                 </screen>
 				To train the english tokenizer use the following command:
 				<screen>
-			    <![CDATA[
-$ opennlp TokenizerTrainer -model en-custom-token.bin -alphaNumOpt true -lang en -data en-custom-token.train -encoding UTF-8
+<![CDATA[$ opennlp TokenizerTrainer -model en-custom-token.bin -alphaNumOpt true -lang en -data en-custom-token.train -encoding UTF-8
 
 Indexing events with TwoPass using cutoff of 5
 
@@ -293,30 +276,27 @@ Path: en-custom-token.bin]]>
 		</section>
 		<section id="tools.tokenizer.training.api">
 			<title>Training API</title>
-            <para>
-                The Tokenizer offers an API to train a new tokenization model. Basically three steps
-                are necessary to train it:
-                <itemizedlist>
-                    <listitem>
-                        <para>The application must open a sample data stream</para>
-                    </listitem>
-                    <listitem>
-                        <para>Call the TokenizerME.train method</para>
-                    </listitem>
-                    <listitem>
-                        <para>Save the TokenizerModel to a file or directly use it</para>
-                    </listitem>
-                </itemizedlist>
-                The following sample code illustrates these steps:
-                <programlisting language="java">
-                    <![CDATA[
-ObjectStream<String> lineStream = new PlainTextByLineStream(new MarkableFileInputStreamFactory(new File("en-custom-sent.train")),
+				<para>
+					The Tokenizer offers an API to train a new tokenization model. Basically three steps
+					are necessary to train it:
+					<itemizedlist>
+						<listitem>
+							<para>The application must open a sample data stream</para>
+						</listitem>
+						<listitem>
+							<para>Call the TokenizerME.train method</para>
+						</listitem>
+						<listitem>
+							<para>Save the TokenizerModel to a file or directly use it</para>
+						</listitem>
+					</itemizedlist>
+					The following sample code illustrates these steps:
+					<programlisting language="java">
+<![CDATA[ObjectStream<String> lineStream = new PlainTextByLineStream(new MarkableFileInputStreamFactory(new File("en-custom-sent.train")),
     StandardCharsets.UTF_8);
 
 ObjectStream<TokenSample> sampleStream = new TokenSampleStream(lineStream);
-
 TokenizerModel model;
-
 try {
   model = TokenizerME.train(sampleStream,
   	TokenizerFactory.create(null, "eng", null, true, null), TrainingParameters.defaultParams());
@@ -333,8 +313,8 @@ try {
   if (modelOut != null)
      modelOut.close();
 }]]>
-                </programlisting>
-            </para>
+				</programlisting>
+			</para>
 		</section>
 	</section>
 	
@@ -369,19 +349,16 @@ try {
 		The following sample will illustrate how the detokenizer with a small
 		rule dictionary (illustration format, not the xml data format):
 		<programlisting>
-			<![CDATA[
-. MERGE_TO_LEFT
+<![CDATA[. MERGE_TO_LEFT
 " RIGHT_LEFT_MATCHING]]>		
 		</programlisting>
 		The dictionary should be used to de-tokenize the following whitespace tokenized sentence:
 		<programlisting>
-			<![CDATA[
-He said " This is a test " .]]>		
+<![CDATA[He said " This is a test " .]]>
 		</programlisting>
 		The tokens would get these tags based on the dictionary:
 		<programlisting>
-			<![CDATA[
-He -> NO_OPERATION
+<![CDATA[He -> NO_OPERATION
 said -> NO_OPERATION
 " -> MERGE_TO_RIGHT
 This -> NO_OPERATION
@@ -393,8 +370,7 @@ test -> NO_OPERATION
 			</programlisting>
 			That will result in the following character sequence:
 		<programlisting>
-			<![CDATA[
-He said "This is a test".]]>		
+<![CDATA[He said "This is a test".]]>
 		</programlisting>
 		</para>
 		<section id="tools.tokenizer.detokenizing.api">
@@ -405,8 +381,7 @@ He said "This is a test".]]>
 				a DetokenizationDictionary (the rule of dictionary) must be created first.
 				The following code sample shows how a rule dictionary can be loaded.
 				<programlisting language="java">
-					<![CDATA[
-InputStream dictIn = new FileInputStream("latin-detokenizer.xml");
+<![CDATA[InputStream dictIn = new FileInputStream("latin-detokenizer.xml");
 DetokenizationDictionary dict = new DetokenizationDictionary(dictIn);]]>
 				</programlisting>
 				After the rule dictionary is loaded the DictionaryDetokenizer can be instantiated.
@@ -416,29 +391,25 @@ Detokenizer detokenizer = new DictionaryDetokenizer(dict);]]>
 				</programlisting>
 				The detokenizer offers two detokenize methods, the first detokenize the input tokens into a String.
 				<programlisting language="java">
-					<![CDATA[
-String[] tokens = new String[]{"A", "co", "-", "worker", "helped", "."};
+<![CDATA[String[] tokens = new String[]{"A", "co", "-", "worker", "helped", "."};
 String sentence = detokenizer.detokenize(tokens, null);
 Assert.assertEquals("A co-worker helped.", sentence);]]>
 				</programlisting>
 				Tokens which are connected without a space in-between can be separated by a split marker.
 				<programlisting language="java">
-					<![CDATA[
-String sentence = detokenizer.detokenize(tokens, "<SPLIT>");
+<![CDATA[String sentence = detokenizer.detokenize(tokens, "<SPLIT>");
 Assert.assertEquals("A co<SPLIT>-<SPLIT>worker helped<SPLIT>.", sentence);]]>
 				</programlisting>
 				The API also offers a method which simply returns operations array in the input tokens array.
 				<programlisting language="java">
-					<![CDATA[
-DetokenizationOperation[] operations = detokenizer.detokenize(tokens);
+<![CDATA[DetokenizationOperation[] operations = detokenizer.detokenize(tokens);
 for (DetokenizationOperation operation : operations) {
   System.out.println(operation);
 }]]>
 				</programlisting>
 				Output:
 				<programlisting>
-					<![CDATA[
-NO_OPERATION
+<![CDATA[NO_OPERATION
 NO_OPERATION
 MERGE_BOTH
 NO_OPERATION
@@ -456,8 +427,7 @@ MERGE_TO_LEFT]]>
 				should be used for the provided tokens.
 				The following code sample shows how a rule dictionary can be created.
 				<programlisting language="java">
-					<![CDATA[
-String[] tokens = new String[]{".", "!", "(", ")", "\"", "-"};
+<![CDATA[String[] tokens = new String[]{".", "!", "(", ")", "\"", "-"};
 Operation[] operations = new Operation[]{
     Operation.MOVE_LEFT,
     Operation.MOVE_LEFT,
diff --git a/opennlp-docs/src/docbkx/uima-integration.xml b/opennlp-docs/src/docbkx/uima-integration.xml
index e29162ced..c021faf31 100644
--- a/opennlp-docs/src/docbkx/uima-integration.xml
+++ b/opennlp-docs/src/docbkx/uima-integration.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
-"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
+<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V5.0//EN"
+"https://docbook.org/xml/5.0.1/dtd/docbook.dtd"[
 ]>
 <!--
 Licensed to the Apache Software Foundation (ASF) under one
@@ -44,8 +44,7 @@ under the License.
 			and build it as shown below. Note the models will be downloaded
 			from the old SourceForge repository and are not licensed under the AL 2.0.
 			<screen>
-			<![CDATA[
-$ ant -f createPear.xml 
+<![CDATA[$ ant -f createPear.xml
 Buildfile: createPear.xml
 
 createPear:
diff --git a/opennlp-docs/src/main/resources/xsl/epub3.xsl b/opennlp-docs/src/main/resources/xsl/epub3.xsl
new file mode 100644
index 000000000..74cf70391
--- /dev/null
+++ b/opennlp-docs/src/main/resources/xsl/epub3.xsl
@@ -0,0 +1,28 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+-->
+
+<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
+  <xsl:import href="urn:docbkx:stylesheet"/>
+
+  <!-- Workaround to create working epub3 files, see: https://github.com/mimil/docbkx-tools/issues/130 -->
+  <xsl:variable name="epub.oebps.dir" select="'./'"/>
+
+</xsl:stylesheet>
\ No newline at end of file
diff --git a/opennlp-docs/src/main/resources/xsl/pdf.xsl b/opennlp-docs/src/main/resources/xsl/pdf.xsl
new file mode 100644
index 000000000..0c6e74d16
--- /dev/null
+++ b/opennlp-docs/src/main/resources/xsl/pdf.xsl
@@ -0,0 +1,78 @@
+<?xml version="1.0" encoding="utf-8"?>
+
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one
+   or more contributor license agreements.  See the NOTICE file
+   distributed with this work for additional information
+   regarding copyright ownership.  The ASF licenses this file
+   to you under the Apache License, Version 2.0 (the
+   "License"); you may not use this file except in compliance
+   with the License.  You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing,
+   software distributed under the License is distributed on an
+   "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+   KIND, either express or implied.  See the License for the
+   specific language governing permissions and limitations
+   under the License.
+-->
+
+<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
+  <xsl:import href="urn:docbkx:stylesheet"/>
+  <xsl:import href="urn:docbkx:stylesheet/highlight.xsl"/>
+
+  <!-- Defining general document properties -->
+  <xsl:param name="paper.type">A4</xsl:param>
+
+  <!-- Defining global options -->
+  <xsl:param name="fop1.extensions" select="1"/>
+  <xsl:param name="tablecolumns.extension" select="1"/>
+  <xsl:param name="default.table.width">100%</xsl:param>
+  <xsl:param name="body.start.indent">0.5cm</xsl:param>
+
+  <xsl:attribute-set name="component.title.properties">
+    <xsl:attribute name="start-indent">0cm</xsl:attribute>
+  </xsl:attribute-set>
+
+  <!-- Customizing indentation of section and sub-section titles -->
+  <xsl:attribute-set name="section.properties">
+    <xsl:attribute name="start-indent">0.0cm</xsl:attribute>
+  </xsl:attribute-set>
+  <xsl:attribute-set name="section.title.properties">
+    <xsl:attribute name="start-indent">0.0cm</xsl:attribute>
+  </xsl:attribute-set>
+  <xsl:attribute-set name="section.level1.properties">
+    <xsl:attribute name="start-indent">0.0cm</xsl:attribute>
+  </xsl:attribute-set>
+  <xsl:attribute-set name="section.title.level1.properties">
+    <xsl:attribute name="start-indent">0cm</xsl:attribute>
+    <xsl:attribute name="border-top">0.5pt solid black</xsl:attribute>
+    <xsl:attribute name="border-bottom">0.5pt solid black</xsl:attribute>
+    <xsl:attribute name="padding-top">6pt</xsl:attribute>
+    <xsl:attribute name="padding-bottom">6pt</xsl:attribute>
+  </xsl:attribute-set>
+  <xsl:attribute-set name="section.title.level2.properties">
+    <xsl:attribute name="start-indent">0cm</xsl:attribute>
+  </xsl:attribute-set>
+
+  <!-- Customizing screen & programlisting -->
+  <xsl:attribute-set name="monospace.verbatim.properties">
+    <xsl:attribute name="font-family">Lucida Sans Typewriter</xsl:attribute>
+    <xsl:attribute name="font-size">9pt</xsl:attribute>
+    <xsl:attribute name="wrap-option">wrap</xsl:attribute>
+    <xsl:attribute name="hyphenation-character">\</xsl:attribute>
+    <xsl:attribute name="keep-together.within-column">always</xsl:attribute>
+  </xsl:attribute-set>
+  <!-- Shading boxes for verbatim envs -->
+  <xsl:param name="shade.verbatim" select="1"/>
+  <xsl:attribute-set name="shade.verbatim.style">
+    <xsl:attribute name="background-color">#E0E0E0</xsl:attribute>
+    <xsl:attribute name="border-width">0.5pt</xsl:attribute>
+    <xsl:attribute name="border-style">solid</xsl:attribute>
+    <xsl:attribute name="border-color">#575757</xsl:attribute>
+    <xsl:attribute name="padding">2pt</xsl:attribute>
+  </xsl:attribute-set>
+
+</xsl:stylesheet>
\ No newline at end of file