OPENNLP-1566 - Array writing error in code example

opennlp-docs/src/docbkx/chunker.xml

@@ -98,26 +98,26 @@ ChunkerME chunker = new ChunkerME(model);]]>
 	   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",
+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",
     "jetliners", "." };
 
-String pos[] = new String[] { "NNP", "NNP", "NNP", "POS", "NNP", "NN",
+String[] pos = new String[] { "NNP", "NNP", "NNP", "POS", "NNP", "NN",
     "VBD", "PRP", "VBD", "DT", "JJ", "NN", "VBG", "PRP$", "NN", "IN",
     "NNP", "NNP", "TO", "VB", "JJ", "NNS", "IN", "NNP", "POS", "CD", "NNS",
     "." };
 
-String tag[] = chunker.chunk(sent, pos);]]>
+String[] tag = chunker.chunk(sent, pos);]]>
 			</programlisting>
 			The tags array contains one chunk tag for each token in the input array. The corresponding
 			tag can be found at the same index as the token has in the input array.
 			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();]]>
+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
@@ -130,7 +130,7 @@ double probs[] = chunker.probs();]]>
 			It can be called in a similar way as chunk.
 			<programlisting language="java">
 		  <![CDATA[
-Sequence topSequences[] = chunk.topKSequences(sent, pos);]]>
+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 

opennlp-docs/src/docbkx/introduction.xml

@@ -81,7 +81,7 @@ ToolName toolName = new ToolName(model);]]>
         and the input is a String or an array of String.
         <programlisting language="java">
                 <![CDATA[
-String output[] = toolName.executeTask("This is a sample text.");]]>
+String[] output = toolName.executeTask("This is a sample text.");]]>
         </programlisting>
         </para>
     </section>

opennlp-docs/src/docbkx/namefinder.xml

@@ -130,7 +130,7 @@ for (String document[][] : documents) {
 			 the following snippet shows a call to find
 			 <programlisting language="java">
 				<![CDATA[
-String sentence[] = new String[]{
+String[] sentence = new String[]{
     "Pierre",
     "Vinken",
     "is",
@@ -140,7 +140,7 @@ String sentence[] = new String[]{
     "."
     };
 
-Span nameSpans[] = nameFinder.find(sentence);]]>
+Span[] nameSpans = nameFinder.find(sentence);]]>
 			</programlisting>
 			The nameSpans arrays contains now exactly one Span which marks the name Pierre Vinken. 
 			The elements between the start and end offsets are the name tokens. In this case the start

opennlp-docs/src/docbkx/parser.xml

@@ -111,7 +111,7 @@ Parser parser = ParserFactory.create(model);]]>
 				<programlisting language="java">
 				<![CDATA[
 String sentence = "The quick brown fox jumps over the lazy dog .";
-Parse topParses[] = ParserTool.parseLine(sentence, parser, 1);]]>
+Parse[] topParses = ParserTool.parseLine(sentence, parser, 1);]]>
 		</programlisting>
 
 		The topParses array only contains one parse because the number of parses is set to 1.

opennlp-docs/src/docbkx/postagger.xml

@@ -86,17 +86,17 @@ POSTaggerME tagger = new POSTaggerME(model);]]>
 	   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",
+String[] sent = new String[]{"Most", "large", "cities", "in", "the", "US", "had",
                              "morning", "and", "afternoon", "newspapers", "."};		  
-String tags[] = tagger.tag(sent);]]>
+String[] tags = tagger.tag(sent);]]>
 			</programlisting>
 			The tags array contains one part-of-speech tag for each token in the input array. The corresponding
 			tag can be found at the same index as the token has in the input array.
 			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();]]>
+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
@@ -109,7 +109,7 @@ double probs[] = tagger.probs();]]>
 			It can be called in a similar way as tag.
 			<programlisting language="java">
 		  <![CDATA[
-Sequence topSequences[] = tagger.topKSequences(sent);]]>
+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 

opennlp-docs/src/docbkx/sentdetect.xml

@@ -94,14 +94,14 @@ SentenceDetectorME sentenceDetector = new SentenceDetectorME(model);]]>
 		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. ");]]>
+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. ");]]>
+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.

opennlp-docs/src/docbkx/tokenizer.xml

@@ -171,7 +171,7 @@ Tokenizer tokenizer = new TokenizerME(model);]]>
 			Strings, where each String is one token.
 			<programlisting language="java">
 			<![CDATA[
-String tokens[] = tokenizer.tokenize("An input sample sentence.");]]>
+String[] tokens = tokenizer.tokenize("An input sample sentence.");]]>
 		 </programlisting>
 			The output will be an array with these tokens.
 			<programlisting>
@@ -183,7 +183,7 @@ String tokens[] = tokenizer.tokenize("An input sample sentence.");]]>
 			String.
 			<programlisting language="java">
 			<![CDATA[
-Span tokenSpans[] = tokenizer.tokenizePos("An input sample sentence.");]]>		
+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.
@@ -195,8 +195,8 @@ Span tokenSpans[] = tokenizer.tokenizePos("An input sample sentence.");]]>
 			<![CDATA[
 TokenizerME tokenizer = ...
 
-String tokens[] = tokenizer.tokenize(...);
-double tokenProbs[] = tokenizer.getTokenProbabilities();]]>
+String[] tokens = tokenizer.tokenize(...);
+double[] tokenProbs = tokenizer.getTokenProbabilities();]]>
 			</programlisting>
 			The tokenProbs array now contains one double value per token, the
 			value is between 0 and 1, where 1 is the highest possible probability