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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

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package edu.stanford.nlp.coref.statistical;

import java.io.File;
import java.io.PrintWriter;
import java.util.Map;

import edu.stanford.nlp.coref.statistical.SimpleLinearClassifier.LearningRateSchedule;
import edu.stanford.nlp.coref.statistical.SimpleLinearClassifier.Loss;

import edu.stanford.nlp.stats.Counter;

/**
 * Pairwise mention-classification model.
 * @author Kevin Clark
 */
public class PairwiseModel {
  public final String name;
  private final int trainingExamples;
  private final int epochs;
  protected final SimpleLinearClassifier classifier;
  private final double singletonRatio;
  private final String str;
  protected final MetaFeatureExtractor meta;

  public static class Builder {
    private final String name;
    private final MetaFeatureExtractor meta;
    @SuppressWarnings("unused") // output in config file with reflection
    private final String source = StatisticalCorefTrainer.extractedFeaturesFile;

    private int trainingExamples = 100000000;
    private int epochs = 8;
    private Loss loss = SimpleLinearClassifier.log();
    private LearningRateSchedule learningRateSchedule =
        SimpleLinearClassifier.adaGrad(0.05, 30.0);
    private double regularizationStrength = 1e-7;
    private double singletonRatio = 0.3;
    private String modelFile = null;

    public Builder(String name, MetaFeatureExtractor meta) {
      this.name = name;
      this.meta = meta;
    }

    public Builder trainingExamples(int trainingExamples)
      { this.trainingExamples = trainingExamples; return this; }
    public Builder epochs(int epochs)
      { this.epochs = epochs; return this; }
    public Builder singletonRatio(double singletonRatio)
      { this.singletonRatio = singletonRatio; return this; }
    public Builder loss(Loss loss)
      { this.loss = loss; return this; }
    public Builder regularizationStrength(double regularizationStrength)
      { this.regularizationStrength = regularizationStrength; return this; }
    public Builder learningRateSchedule(LearningRateSchedule learningRateSchedule)
      { this.learningRateSchedule = learningRateSchedule; return this; }
    public Builder modelPath(String modelFile)
      { this.modelFile = modelFile; return this; }

    public PairwiseModel build() {
      return new PairwiseModel(this);
    }
  }

  public static Builder newBuilder(String name, MetaFeatureExtractor meta) {
    return new Builder(name, meta);
  }

  public PairwiseModel(Builder builder) {
    name = builder.name;
    meta = builder.meta;
    trainingExamples = builder.trainingExamples;
    epochs = builder.epochs;
    singletonRatio = builder.singletonRatio;
    classifier = new SimpleLinearClassifier(builder.loss, builder.learningRateSchedule,
        builder.regularizationStrength, builder.modelFile == null ? null :
          ((builder.modelFile.endsWith(".ser") || builder.modelFile.endsWith(".gz"))  ? builder.modelFile :
          StatisticalCorefTrainer.pairwiseModelsPath + builder.modelFile + "/model.ser"));
    str = StatisticalCorefTrainer.fieldValues(builder);
  }

  public String getDefaultOutputPath() {
    return StatisticalCorefTrainer.pairwiseModelsPath + name +"/";
  }

  public SimpleLinearClassifier getClassifier() {
    return classifier;
  }

  public void writeModel() throws Exception {
    writeModel(getDefaultOutputPath());
  }

  public void writeModel(String outputPath) throws Exception {
    File outDir = new File(outputPath);
    if (!outDir.exists()) {
      outDir.mkdir();
    }

    try (PrintWriter writer = new PrintWriter(outputPath + "config", "UTF-8")) {
      writer.print(str);
    }

    try (PrintWriter writer = new PrintWriter(outputPath + "/weights", "UTF-8")) {
      classifier.printWeightVector(writer);
    }

    classifier.writeWeights(outputPath + "/model.ser");
  }

  public void learn(Example example,
      Map mentionFeatures, Compressor compressor) {
    Counter features = meta.getFeatures(example, mentionFeatures, compressor);
    classifier.learn(features, example.label == 1.0 ? 1.0 : -1.0, 1.0);
  }

  public void learn(Example example,
      Map mentionFeatures, Compressor compressor,
      double weight) {
    Counter features = meta.getFeatures(example, mentionFeatures, compressor);
    classifier.learn(features, example.label == 1.0 ? 1.0 : -1.0, weight);
  }

  public void learn(Example correct, Example incorrect,
      Map mentionFeatures, Compressor compressor,
      double weight) {
    Counter cFeatures = null;
    Counter iFeatures = null;
    if (correct != null) {
      cFeatures = meta.getFeatures(correct, mentionFeatures, compressor);
    }
    if (incorrect != null) {
      iFeatures = meta.getFeatures(incorrect, mentionFeatures, compressor);
    }

    if (correct == null || incorrect == null) {
      if (singletonRatio != 0) {
        if (correct != null) {
          classifier.learn(cFeatures, 1.0, weight * singletonRatio);
        }
        if (incorrect != null) {
          classifier.learn(iFeatures, -1.0, weight * singletonRatio);
        }
      }
    } else {
      classifier.learn(cFeatures, 1.0, weight);
      classifier.learn(iFeatures, -1.0, weight);
    }
  }

  public double predict(Example example,
      Map mentionFeatures, Compressor compressor) {
    Counter features = meta.getFeatures(example, mentionFeatures, compressor);
    return classifier.label(features);
  }

  public int getNumTrainingExamples() {
    return trainingExamples;
  }

  public int getNumEpochs() {
    return epochs;
  }
}