edu.stanford.nlp.ie.KBPEnsembleExtractor 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;


import edu.stanford.nlp.classify.Classifier;
import edu.stanford.nlp.classify.LinearClassifier;
import edu.stanford.nlp.io.IOUtils;
import edu.stanford.nlp.io.RuntimeIOException;
import edu.stanford.nlp.pipeline.DefaultPaths;
import edu.stanford.nlp.util.ArgumentParser;
import edu.stanford.nlp.util.Pair;
import edu.stanford.nlp.util.logging.Redwood;
import edu.stanford.nlp.util.logging.RedwoodConfiguration;

import java.io.*;
import java.util.List;
import java.util.Optional;

/**
 * An ensemble of other KBP relation extractors.
 * Currently, this class just takes the union of the given extractors.
 * That is, it returns the first relation returned by any extractor
 * (ties broken by the order the extractors are passed to the constructor),
 * and only returns no_relation if no extractor proposed a relation.
 */
@SuppressWarnings("FieldCanBeLocal")
public class KBPEnsembleExtractor implements KBPRelationExtractor {
  protected static final Redwood.RedwoodChannels logger = Redwood.channels(KBPRelationExtractor.class);

  @ArgumentParser.Option(name="model", gloss="The path to the model")
  private static String STATISTICAL_MODEL = DefaultPaths.DEFAULT_KBP_CLASSIFIER;

  @ArgumentParser.Option(name="semgrex", gloss="Semgrex patterns directory")
  private static String SEMGREX_DIR = DefaultPaths.DEFAULT_KBP_SEMGREX_DIR;

  @ArgumentParser.Option(name="tokensregex", gloss="Tokensregex patterns directory")
  private static String TOKENSREGEX_DIR = DefaultPaths.DEFAULT_KBP_TOKENSREGEX_DIR;

  @ArgumentParser.Option(name="predictions", gloss="Dump model predictions to this file")
  public static Optional PREDICTIONS = Optional.empty();

  @ArgumentParser.Option(name="test", gloss="The dataset to test on")
  public static File TEST_FILE = new File("test.conll");

  /**
   * The extractors to run, in the order of priority they should be run in.
   */
  public final KBPRelationExtractor[] extractors;

  /**
   * Creates a new ensemble extractor from the given argument extractors.
   * @param extractors A varargs list of extractors to union together.
   */
  public KBPEnsembleExtractor(KBPRelationExtractor... extractors) {
    this.extractors = extractors;
  }

  @Override
  public Pair classify(KBPInput input) {
    Pair prediction = Pair.makePair(KBPRelationExtractor.NO_RELATION, 1.0);
    for (KBPRelationExtractor extractor : extractors) {
      Pair classifierPrediction = extractor.classify(input);
      if (prediction.first.equals(KBPRelationExtractor.NO_RELATION) ||
          (!classifierPrediction.first.equals(KBPRelationExtractor.NO_RELATION) &&
              classifierPrediction.second > prediction.second)
          ){
        // The last prediction was NO_RELATION, or this is not NO_RELATION and has a higher score
        prediction = classifierPrediction;
      }
    }
    return prediction;
  }

  public static void main(String[] args) throws IOException, ClassNotFoundException {
    RedwoodConfiguration.standard().apply();  // Disable SLF4J crap.
    ArgumentParser.fillOptions(KBPEnsembleExtractor.class, args);

    Object object = IOUtils.readObjectFromURLOrClasspathOrFileSystem(STATISTICAL_MODEL);
    KBPRelationExtractor statisticalExtractor;
    if (object instanceof LinearClassifier) {
      //noinspection unchecked
      statisticalExtractor = new KBPStatisticalExtractor((Classifier) object);
    } else if (object instanceof KBPStatisticalExtractor) {
      statisticalExtractor = (KBPStatisticalExtractor) object;
    } else {
      throw new ClassCastException(object.getClass() + " cannot be cast into a " + KBPStatisticalExtractor.class);
    }
    logger.info("Read statistical model from " + STATISTICAL_MODEL);
    KBPRelationExtractor extractor = new KBPEnsembleExtractor(
        new KBPTokensregexExtractor(TOKENSREGEX_DIR),
        new KBPSemgrexExtractor(SEMGREX_DIR),
        statisticalExtractor
    );

    List> testExamples = KBPRelationExtractor.readDataset(TEST_FILE);

    extractor.computeAccuracy(testExamples.stream(), PREDICTIONS.map(x -> {
      try {
        return "stdout".equalsIgnoreCase(x) ? System.out : new PrintStream(new FileOutputStream(x));
      } catch (IOException e) {
        throw new RuntimeIOException(e);
      }
    }));

  }
}