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new c49fdb44 OPENNLP-1566 - Array writing error in code example (#605)
c49fdb44 is described below
commit c49fdb44fa91227c7ebdc0351405ec12a36d645a
Author: ShellRean <[email protected]>
AuthorDate: Tue Jun 18 14:19:22 2024 +0700
OPENNLP-1566 - Array writing error in code example (#605)
fix missformat java declaration array
---
opennlp-docs/src/docbkx/chunker.xml | 10 +++++-----
opennlp-docs/src/docbkx/introduction.xml | 2 +-
opennlp-docs/src/docbkx/namefinder.xml | 4 ++--
opennlp-docs/src/docbkx/parser.xml | 2 +-
opennlp-docs/src/docbkx/postagger.xml | 8 ++++----
opennlp-docs/src/docbkx/sentdetect.xml | 4 ++--
opennlp-docs/src/docbkx/tokenizer.xml | 8 ++++----
7 files changed, 19 insertions(+), 19 deletions(-)
diff --git a/opennlp-docs/src/docbkx/chunker.xml
b/opennlp-docs/src/docbkx/chunker.xml
index 5c65deac..44481992 100644
--- a/opennlp-docs/src/docbkx/chunker.xml
+++ b/opennlp-docs/src/docbkx/chunker.xml
@@ -98,18 +98,18 @@ 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.
@@ -117,7 +117,7 @@ String tag[] = chunker.chunk(sent, pos);]]>
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
diff --git a/opennlp-docs/src/docbkx/introduction.xml
b/opennlp-docs/src/docbkx/introduction.xml
index 5187039a..1aee630a 100644
--- a/opennlp-docs/src/docbkx/introduction.xml
+++ b/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>
diff --git a/opennlp-docs/src/docbkx/namefinder.xml
b/opennlp-docs/src/docbkx/namefinder.xml
index 8566467e..cdf77d3f 100644
--- a/opennlp-docs/src/docbkx/namefinder.xml
+++ b/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
diff --git a/opennlp-docs/src/docbkx/parser.xml
b/opennlp-docs/src/docbkx/parser.xml
index f5dd8c49..2dc1ecd6 100644
--- a/opennlp-docs/src/docbkx/parser.xml
+++ b/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.
diff --git a/opennlp-docs/src/docbkx/postagger.xml
b/opennlp-docs/src/docbkx/postagger.xml
index 69eacc60..5f045e4f 100644
--- a/opennlp-docs/src/docbkx/postagger.xml
+++ b/opennlp-docs/src/docbkx/postagger.xml
@@ -86,9 +86,9 @@ 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.
@@ -96,7 +96,7 @@ String tags[] = tagger.tag(sent);]]>
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
diff --git a/opennlp-docs/src/docbkx/sentdetect.xml
b/opennlp-docs/src/docbkx/sentdetect.xml
index ee7868eb..4e3a1db6 100644
--- a/opennlp-docs/src/docbkx/sentdetect.xml
+++ b/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.
diff --git a/opennlp-docs/src/docbkx/tokenizer.xml
b/opennlp-docs/src/docbkx/tokenizer.xml
index 32d4f241..3627d825 100644
--- a/opennlp-docs/src/docbkx/tokenizer.xml
+++ b/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
