Examples of opennlp.model.EventStream

opennlp.model.EventStream
A object which can deliver a stream of training events for the GIS procedure (or others such as IIS if and when they are implemented). EventStreams don't need to use opennlp.maxent.DataStreams, but doing so would provide greater flexibility for producing events from data stored in different formats.

      return new Event(obs.substring(lastSpace+1),contexts,values);
    }
  }


  public static void main(String[] args) throws java.io.IOException {
    EventStream es = new RealBasicEventStream(new PlainTextByLineDataStream(new java.io.FileReader(args[0])));
    while (es.hasNext()) {
      System.out.println(es.next());
    }
  }

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      throws IOException {
    Factory factory = new Factory();


    Map<String, String> manifestInfoEntries = new HashMap<String, String>();


    EventStream eventStream = new TokSpanEventStream(samples,
        useAlphaNumericOptimization, factory.getAlphanumeric(languageCode),
        factory.createTokenContextGenerator(languageCode,
            getAbbreviations(abbreviations)));


    AbstractModel maxentModel = TrainUtil.train(eventStream,

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      String modelFileName =
        dataFileName.substring(0,dataFileName.lastIndexOf('.'))
        + "Model.txt";
      try {
        FileReader datafr = new FileReader(new File(dataFileName));
        EventStream es;
        if (!real) { 
          es = new BasicEventStream(new PlainTextByLineDataStream(datafr));
        }
        else {
          es = new RealBasicEventStream(new PlainTextByLineDataStream(datafr));

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       featureGenerator = createFeatureGenerator();
     
     AbstractModel nameFinderModel;
     
     if (!TrainUtil.isSequenceTraining(trainParams.getSettings())) {
       EventStream eventStream = new NameFinderEventStream(samples, type,
           new DefaultNameContextGenerator(featureGenerator));
       
       nameFinderModel = TrainUtil.train(eventStream, trainParams.getSettings(), manifestInfoEntries);
     }
     else {

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  public static final void main(String[] args) throws java.io.IOException {
    if (args.length != 0) {
      System.err.println("Usage: NameFinderEventStream < training files");
      System.exit(1);
    }
    EventStream es = new NameFinderEventStream(new NameSampleDataStream(
        new PlainTextByLineStream(new java.io.InputStreamReader(System.in))));
    while (es.hasNext()) {
      System.out.println(es.next());
    }
  }

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    AbstractModel posModel;
    
    if (!TrainUtil.isSequenceTraining(trainParams.getSettings())) {
      
      EventStream es = new POSSampleEventStream(samples, contextGenerator);
      
      posModel = TrainUtil.train(es, trainParams.getSettings(), manifestInfoEntries);
    }
    else {
      POSSampleSequenceStream ss = new POSSampleSequenceStream(samples, contextGenerator);

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      ChunkerContextGenerator contextGenerator, TrainingParameters mlParams)
  throws IOException {
    
    Map<String, String> manifestInfoEntries = new HashMap<String, String>();
    
    EventStream es = new ChunkerEventStream(in, contextGenerator);
    
    AbstractModel maxentModel = TrainUtil.train(es, mlParams.getSettings(), manifestInfoEntries);
    
    return new ChunkerModel(lang, maxentModel, manifestInfoEntries);
  }

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  public static ChunkerModel train(String lang, ObjectStream<ChunkSample> in,
      TrainingParameters mlParams, ChunkerFactory factory) throws IOException {


    Map<String, String> manifestInfoEntries = new HashMap<String, String>();


    EventStream es = new ChunkerEventStream(in, factory.getContextGenerator());


    AbstractModel maxentModel = TrainUtil.train(es, mlParams.getSettings(),
        manifestInfoEntries);


    return new ChunkerModel(lang, maxentModel, manifestInfoEntries, factory);

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      ChunkerContextGenerator contextGenerator, TrainingParameters mlParams)
  throws IOException {
    
    Map<String, String> manifestInfoEntries = new HashMap<String, String>();
    
    EventStream es = new ChunkerEventStream(in, contextGenerator);
    
    AbstractModel maxentModel = TrainUtil.train(es, mlParams.getSettings(), manifestInfoEntries);
    
    return new ChunkerModel(lang, maxentModel, manifestInfoEntries);
  }

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        + "Model.txt";
      File outputFile = new File(modelFileName);
      AbstractModelWriter writer = null;
      try {
        FileReader datafr = new FileReader(new File(dataFileName));
        EventStream es;
        if (!real) { 
          es = new BasicEventStream(new PlainTextByLineDataStream(datafr));
        }
        else {
          es = new RealBasicEventStream(new PlainTextByLineDataStream(datafr));

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Related Classes of opennlp.model.EventStream

com.tamingtext.qa.AnswerTypeEventStream

CreateModel

opennlp.maxent.ModelApplier

opennlp.maxent.ModelTrainer

opennlp.maxent.RealBasicEventStream

opennlp.maxent.ScaleDoesntMatterTest

opennlp.PrepAttachDataUtil

opennlp.tools.chunker.ChunkerME

opennlp.tools.namefind.NameFinderEventStream

opennlp.tools.namefind.NameFinderEventStreamTest

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