edu.stanford.nlp.ie.crf.TestSequenceModel Maven / Gradle / Ivy

Show more of this group Show more artifacts with this name
Show all versions of stanford-corenlp Show documentation

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.

There is a newer version: 4.5.7

Show newest version

package edu.stanford.nlp.ie.crf;

import java.util.List;

import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.sequences.SequenceModel;
import edu.stanford.nlp.util.CoreMap;

/**
 * For sequence model inference at test-time.
 *
 * @author Spence Green
 */
public class TestSequenceModel implements SequenceModel {

  private final int window;
  // private final int numClasses;
  private final CRFCliqueTree cliqueTree;  // todo [cdm 2014]: Just make String?
  private final int[] backgroundTag;

  private final int[] allTags;
  private final int[][] allowedTagsAtPosition;

  public TestSequenceModel(CRFCliqueTree cliqueTree) {
    this(cliqueTree, null, null);
  }

  public TestSequenceModel(CRFCliqueTree cliqueTree,
      LabelDictionary labelDictionary, List document) {
    // this.factorTables = factorTables;
    this.cliqueTree = cliqueTree;
    // this.window = factorTables[0].windowSize();
    this.window = cliqueTree.window();
    // this.numClasses = factorTables[0].numClasses();
    int numClasses = cliqueTree.getNumClasses();

    this.backgroundTag = new int[] { cliqueTree.backgroundIndex() };
    allTags = new int[numClasses];
    for (int i = 0; i < allTags.length; i++) {
      allTags[i] = i;
    }
    if (labelDictionary != null) {
      // Constrained
      allowedTagsAtPosition = new int[document.size()][];
      for (int i = 0; i < allowedTagsAtPosition.length; ++i) {
        CoreMap token  = document.get(i);
        String observation = token.get(CoreAnnotations.TextAnnotation.class);
        allowedTagsAtPosition[i] = labelDictionary.isConstrained(observation) ?
            labelDictionary.getConstrainedSet(observation) : allTags;
      }
    } else {
      allowedTagsAtPosition = null;
    }
  }

  @Override
  public int length() {
    return cliqueTree.length();
  }

  @Override
  public int leftWindow() {
    return window - 1;
  }

  @Override
  public int rightWindow() {
    return 0;
  }

  @Override
  public int[] getPossibleValues(int pos) {
    if (pos < leftWindow()) {
      return backgroundTag;
    }
    int realPos = pos - window + 1;
    return allowedTagsAtPosition == null ? allTags :
      allowedTagsAtPosition[realPos];
  }

  /**
   * Return the score of the proposed tags for position given.
   * @param tags is an array indicating the assignment of labels to score.
   * @param pos is the position to return a score for.
   */
  @Override
  public double scoreOf(int[] tags, int pos) {
    int[] previous = new int[window - 1];
    int realPos = pos - window + 1;
    for (int i = 0; i < window - 1; i++) {
      previous[i] = tags[realPos + i];
    }
    return cliqueTree.condLogProbGivenPrevious(realPos, tags[pos], previous);
  }

  @Override
  public double[] scoresOf(int[] tags, int pos) {
    int[] allowedTags = getPossibleValues(pos);
    int realPos = pos - window + 1;
    int[] previous = new int[window - 1];
    for (int i = 0; i < window - 1; i++) {
      previous[i] = tags[realPos + i];
    }
    double[] scores = new double[allowedTags.length];
    for (int i = 0; i < allowedTags.length; i++) {
      scores[i] = cliqueTree.condLogProbGivenPrevious(realPos, allowedTags[i], previous);
    }
    return scores;
  }

  @Override
  public double scoreOf(int[] sequence) {
    throw new UnsupportedOperationException();
  }

}