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https://issues.apache.org/jira/browse/OPENNLP-1309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jeffrey T. Zemerick closed OPENNLP-1309.
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> NameFinderME - Unexpected result using unchanged training data
> --------------------------------------------------------------
>
> Key: OPENNLP-1309
> URL: https://issues.apache.org/jira/browse/OPENNLP-1309
> Project: OpenNLP
> Issue Type: Bug
> Components: Name Finder
> Affects Versions: 1.9.2
> Reporter: Michael
> Priority: Major
>
>
> Hello,
> Based on
> [NameFinderMETest.java|https://github.com/apache/opennlp/blob/master/opennlp-tools/src/test/java/opennlp/tools/namefind/NameFinderMETest.java]
> / function _testNameFinder()_, I have written a simple test code and changed
> the [test
> sentence|https://github.com/apache/opennlp/blob/master/opennlp-tools/src/test/java/opennlp/tools/namefind/NameFinderMETest.java#L79]
> from *(1)*:
> {code:java}
> String[] sentence = {"Alisa",
> "appreciated",
> "the",
> "hint",
> "and",
> "enjoyed",
> "a",
> "delicious",
> "traditional",
> "meal."};
> {code}
> to *(2)*:
> {code:java}
> String[] sentence = {"Alisa",
> "and",
> "Mike",
> "appreciated",
> "the",
> "hint",
> "and",
> "enjoyed",
> "a",
> "delicious",
> "traditional",
> "meal."};
> {code}
> (Just added "_and Mike_") and expected to get 2 results (two names _Alisa_
> and _Mike_) because both names are annotated in the training data. I just get
> 1 result (Mike) for *(2)*. I used the training data file
> [AnnotatedSentences.txt|https://github.com/apache/opennlp/blob/master/opennlp-tools/src/test/resources/opennlp/tools/namefind/AnnotatedSentences.txt]
> (unchanged).
> Can anyone tell me what's wrong? Thanks.
> h3. +Test code:+
>
> {code:java}
> String trainingDatafilePath = "opennlp/tools/namefind/AnnotatedSentences.txt";
> String encoding = "ISO-8859-1";
> ObjectStream<NameSample> sampleStream = new NameSampleDataStream(new
> PlainTextByLineStream(new MarkableFileInputStreamFactory(new
> File(trainingDatafilePath+"AnnotatedSentences.txt")), encoding));
>
> TrainingParameters params = new TrainingParameters();
> params.put(TrainingParameters.ITERATIONS_PARAM, 70);
> params.put(TrainingParameters.CUTOFF_PARAM, 1);
> TokenNameFinderModel nameFinderModel = NameFinderME.train("eng", null,
> sampleStream,
> params, TokenNameFinderFactory.create(null, null, Collections.emptyMap(),
> new BioCodec()));
> TokenNameFinder nameFinder = new NameFinderME(nameFinderModel);
> // now test if it can detect the sample sentences
> String[] sentence = {"Alisa",
> "and",
> "Mike",
> "appreciated",
> "the",
> "hint",
> "and",
> "enjoyed",
> "a",
> "delicious",
> "traditional",
> "meal."};
> Span[] names = nameFinder.find(sentence);
> if (names != null && names.length != 0) {
> System.out.println(" > Found ["+names.length+"] results");
> for(Span name : names){
> String personName="";
> for(int i=name.getStart(); i<name.getEnd(); i++){
> personName+=sentence[i]+" ";
> }
> System.out.println(" > Result "+1+": Type: ["+name.getType()+"] :
> PersonName: ["+personName+"]\t [probability="+name.getProb()+"]");
> }
> } else {
> System.out.println(" > No results found");
> }
> {code}
>
>
> h3. +Result for (1):+
> Indexing events with TwoPass using cutoff of 1
> Computing event counts... done. 1392 events
> Indexing... done.
> Collecting events... Done indexing in 0.22 s.
> Incorporating indexed data for training...
> done.
> Number of Event Tokens: 1392
> Number of Outcomes: 3
> Number of Predicates: 9164
> Computing model parameters...
> Performing 70 iterations.
> 1: . (1355/1392) 0.9734195402298851
> 2: . (1383/1392) 0.9935344827586207
> 3: . (1390/1392) 0.9985632183908046
> 4: . (1390/1392) 0.9985632183908046
> 5: . (1391/1392) 0.9992816091954023
> 6: . (1392/1392) 1.0
> 7: . (1392/1392) 1.0
> 8: . (1392/1392) 1.0
> 9: . (1392/1392) 1.0
> Stopping: change in training set accuracy less than 1.0E-5
> Stats: (1392/1392) 1.0
> ...done.
> *Found [1] results*
> *Result 1: Type: [default] : PersonName: [Alisa ]
> [probability=0.5483001511243855]*
> h3.
> +Result for (2):+
> Indexing events with TwoPass using cutoff of 1
> Computing event counts... done. 1392 events
> Indexing... done.
> Collecting events... Done indexing in 0.22 s.
> Incorporating indexed data for training...
> done.
> Number of Event Tokens: 1392
> Number of Outcomes: 3
> Number of Predicates: 9164
> Computing model parameters...
> Performing 70 iterations.
> 1: . (1355/1392) 0.9734195402298851
> 2: . (1383/1392) 0.9935344827586207
> 3: . (1390/1392) 0.9985632183908046
> 4: . (1390/1392) 0.9985632183908046
> 5: . (1391/1392) 0.9992816091954023
> 6: . (1392/1392) 1.0
> 7: . (1392/1392) 1.0
> 8: . (1392/1392) 1.0
> 9: . (1392/1392) 1.0
> Stopping: change in training set accuracy less than 1.0E-5
> Stats: (1392/1392) 1.0
> ...done.
> *Found [1] results*
> *Result 1: Type: [default] : PersonName: [Mike ]
> [probability=0.460685209028902]*
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