Examples of opennlp.perceptron.SimplePerceptronSequenceTrainer

opennlp.perceptron.SimplePerceptronSequenceTrainer
Trains models for sequences using the perceptron algorithm. Each outcome is represented as a binary perceptron classifier. This supports standard (integer) weighting as well average weighting. Sequence information is used in a simplified was to that described in: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with the Perceptron Algorithm. Michael Collins, EMNLP 2002. Specifically only updates are applied to tokens which were incorrectly tagged by a sequence tagger rather than to all feature across the sequence which differ from the training sequence.

    int iterations = getIntParam(trainParams, ITERATIONS_PARAM, ITERATIONS_DEFAULT, reportMap);
    int cutoff = getIntParam(trainParams, CUTOFF_PARAM, CUTOFF_DEFAULT, reportMap);
    
    boolean useAverage = getBooleanParam(trainParams, "UseAverage", true, reportMap);
    
    return new SimplePerceptronSequenceTrainer().trainModel(
        iterations, events, cutoff,useAverage);
  }

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  public static AbstractModel trainPerceptronModel(EventStream es, int iterations, int cut) throws IOException {
    return trainPerceptronModel(es,iterations,cut,true);
  }
  
  public static AbstractModel trainPerceptronSequenceModel(SequenceStream ss, int iterations, int cut, boolean useAverage) throws IOException {
    return new SimplePerceptronSequenceTrainer().trainModel(iterations, ss, cut,useAverage);
  }

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            ss = new POSSampleSequenceStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile), encoding))), cg);
          }
        }
        mod = new SimplePerceptronSequenceTrainer().trainModel(iterations, ss, cutoff, true);
        System.out.println("Saving the model as: " + outFile);
        new SuffixSensitivePerceptronModelWriter(mod, outFile).persist();
      }
      else {
        POSSampleEventStream es;

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    int iterations = getIntParam(trainParams, ITERATIONS_PARAM, ITERATIONS_DEFAULT, reportMap);
    int cutoff = getIntParam(trainParams, CUTOFF_PARAM, CUTOFF_DEFAULT, reportMap);
    
    boolean useAverage = getBooleanParam(trainParams, "UseAverage", true, reportMap);
    
    return new SimplePerceptronSequenceTrainer().trainModel(
        iterations, events, cutoff,useAverage);
  }

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  public static AbstractModel trainPerceptronModel(EventStream es, int iterations, int cut) throws IOException {
    return trainPerceptronModel(es,iterations,cut,true);
  }
  
  public static AbstractModel trainPerceptronSequenceModel(SequenceStream ss, int iterations, int cut, boolean useAverage) throws IOException {
    return new SimplePerceptronSequenceTrainer().trainModel(iterations, ss, cut,useAverage);
  }

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            ss = new POSSampleSequenceStream(new WordTagSampleStream((
                new InputStreamReader(new FileInputStream(inFile), encoding))), cg);
          }
        }
        mod = new SimplePerceptronSequenceTrainer().trainModel(iterations, ss, cutoff, true);
        System.out.println("Saving the model as: " + outFile);
        new SuffixSensitivePerceptronModelWriter(mod, outFile).persist();
      }
      else {
        POSSampleEventStream es;

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Related Classes of opennlp.perceptron.SimplePerceptronSequenceTrainer

opennlp.model.DataIndexer

opennlp.model.MutableContext

opennlp.model.OnePassDataIndexer

opennlp.model.SequenceStreamEventStream

opennlp.model.TrainUtil

opennlp.tools.postag.POSTaggerTrainer

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