Examples of opennlp.tools.util.featuregen.WindowFeatureGenerator

opennlp.tools.util.featuregen.WindowFeatureGenerator
Generates previous and next features for a given {@link AdaptiveFeatureGenerator}. The window size can be specified. Features: Current token is always included unchanged Previous tokens are prefixed with p distance Next tokens are prefix with n distance

    File destDir = new File("target");
    
    //<start id="ne-features"/>    
    AggregatedFeatureGenerator featureGenerators = 
      new AggregatedFeatureGenerator( //<co id="co.opennlp.name.createfeat"/>
        new WindowFeatureGenerator(
          new TokenFeatureGenerator(), 2, 2), //<co id="co.opennlp.name.tokenfeat"/>
        new WindowFeatureGenerator(
          new TokenClassFeatureGenerator(), 2, 2), //<co id="co.opennlp.name.tokenclassfeat"/>
        new PreviousMapFeatureGenerator() //<co id="co.opennlp.name.prevfeat"/>
      );  
    /*<calloutlist>
    <callout arearefs="co.opennlp.name.createfeat"><para>Creates an aggregated feature generator containing the 3 generators defined below.</para></callout>

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      contextGenerator = new DefaultNameContextGenerator(featureGenerator);
    }
    
    contextGenerator.addFeatureGenerator(
          new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
    
    if (sequenceValidator == null)
      sequenceValidator = new NameFinderSequenceValidator();
    
    beam = new BeamSearch<String>(beamSize, contextGenerator, this.model,

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  @Deprecated
  public NameFinderME(MaxentModel mod, NameContextGenerator cg, int beamSize) {
    model = mod;
    contextGenerator = cg;


    contextGenerator.addFeatureGenerator(new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
    beam = new BeamSearch<String>(beamSize, cg, mod,
        new NameFinderSequenceValidator(), beamSize);
  }

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  }


  private static AdaptiveFeatureGenerator createFeatureGenerator() {
   return 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)
           });

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   */
  public NameFinderEventStream(ObjectStream<NameSample> dataStream, String type, NameContextGenerator contextGenerator) {
    super(dataStream);
    
    this.contextGenerator = contextGenerator;
    this.contextGenerator.addFeatureGenerator(new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
    
    if (type != null)
      this.type = type;
    else
      this.type = "default";

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      contextGenerator = new DefaultNameContextGenerator(featureGenerator);
    }


    contextGenerator.addFeatureGenerator(
          new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));


    if (sequenceValidator == null)
      sequenceValidator = new NameFinderSequenceValidator();


    beam = new BeamSearch<String>(beamSize, contextGenerator, this.model,

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  @Deprecated
  public NameFinderME(MaxentModel mod, NameContextGenerator cg, int beamSize) {
    model = mod;
    contextGenerator = cg;


    contextGenerator.addFeatureGenerator(new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
    beam = new BeamSearch<String>(beamSize, cg, mod,
        new NameFinderSequenceValidator(), beamSize);
  }

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  }


  private static AdaptiveFeatureGenerator createFeatureGenerator() {
   return 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)
           });

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   */
  public NameFinderEventStream(ObjectStream<NameSample> dataStream, String type, NameContextGenerator contextGenerator) {
    super(dataStream);
    
    this.contextGenerator = contextGenerator;
    this.contextGenerator.addFeatureGenerator(new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
    
    if (type != null)
      this.type = type;
    else
      this.type = "default";

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    this.model = model.getNameFinderSequenceModel();
    contextGenerator = factory.createContextGenerator();


    // TODO: We should deprecate this. And come up with a better solution!
    contextGenerator.addFeatureGenerator(
            new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
  }

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Related Classes of opennlp.tools.util.featuregen.WindowFeatureGenerator

com.tamingtext.opennlp.NameFinderTest

opennlp.tools.namefind.NameFinderEventStream

opennlp.tools.namefind.NameFinderME

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