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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.parser.lexparser; 
import edu.stanford.nlp.util.logging.Redwood;

import java.util.*;

import edu.stanford.nlp.ling.CategoryWordTagFactory;
import edu.stanford.nlp.ling.HasWord;
import edu.stanford.nlp.parser.KBestViterbiParser;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.trees.LabeledScoredTreeFactory;
import edu.stanford.nlp.trees.HeadFinder;
import edu.stanford.nlp.util.*;


/** Provides a much faster way to realize the factored
 *  parsing idea, including easily returning "k good" results
 *  at the expense of optimality.  Exploiting the k best functionality
 *  of the ExhaustivePCFGParser, this model simply gets more than
 *  k best PCFG parsers, scores them according to the dependency
 *  grammar, and returns them in terms of their product score.
 *  No actual parsing is done.
 *
 *  @author Christopher Manning
 */
public class FastFactoredParser implements KBestViterbiParser  {

  /** A logger for this class */
  private static Redwood.RedwoodChannels log = Redwood.channels(FastFactoredParser.class);

  // TODO Regression tests
  // TODO Set dependency tuning and test whether useful
  // TODO Validate and up the Arabic numbers
  // TODO Make the printing options for k good/best sane
  // TODO Check parsing of a List.  Change defaultSentence() to be List

  protected static final boolean VERBOSE = false;

  protected ExhaustivePCFGParser pparser;
  protected GrammarProjection projection;

  protected MLEDependencyGrammar dg;
  protected Options op;

  private int numToFind;

  private final Index wordIndex;
  private final Index tagIndex;

  protected int project(int state) {
    return projection.project(state);
  }

  /**
   * Return the best parse of the sentence most recently parsed.
   *
   * @return The best (highest score) tree
   */
  public Tree getBestParse() {
    return nGoodTrees.get(0).object();
  }

  public double getBestScore() {
    return nGoodTrees.get(0).score();
  }


  public boolean hasParse() {
    return ! nGoodTrees.isEmpty();
  }


  private List> nGoodTrees = new ArrayList<>();



  /**
   * Return the list of N "good" parses of the sentence most recently parsed.
   * (The first is guaranteed to be the best, but later ones are only
   * guaranteed the best subject to the possibilities that disappear because
   * the PCFG/Dep charts only store the best over each span.)
   *
   * @return The list of N best trees
   */
  public List> getKGoodParses(int k) {
    if (k <= nGoodTrees.size()) {
      return nGoodTrees.subList(0, k);
    } else {
      throw new UnsupportedOperationException("FastFactoredParser: cannot provide " + k + " good parses.");
    }
  }


  /** Use the DependencyGrammar to score the tree.
   *
   * @param tr A binarized tree (as returned by the PCFG parser
   * @return The score for the tree according to the grammar
   */
  private double depScoreTree(Tree tr) {
    // log.info("Here's our tree:");
    // tr.pennPrint();
    // log.info(Trees.toDebugStructureString(tr));
    Tree cwtTree = tr.deepCopy(new LabeledScoredTreeFactory(), new CategoryWordTagFactory());
    cwtTree.percolateHeads(binHeadFinder);
    // log.info("Here's what it went to:");
    // cwtTree.pennPrint();
    List deps = MLEDependencyGrammar.treeToDependencyList(cwtTree, wordIndex, tagIndex);
    // log.info("Here's the deps:\n" + deps);
    return dg.scoreAll(deps);
  }

  private final HeadFinder binHeadFinder = new BinaryHeadFinder();

   /**
   * Parse a Sentence.  It is assumed that when this is called, the pparser
   * has already been called to parse the sentence.
   *
   * @param words The list of words to parse.
   * @return true iff it could be parsed
   */
  public boolean parse(List words) {
    nGoodTrees.clear();

    int numParsesToConsider = numToFind * op.testOptions.fastFactoredCandidateMultiplier + op.testOptions.fastFactoredCandidateAddend;
    if (pparser.hasParse()) {
      List> pcfgBest = pparser.getKBestParses(numParsesToConsider);
      Beam> goodParses = new Beam<>(numToFind);

      for (ScoredObject candidate : pcfgBest) {
        if (Thread.interrupted()) {
          throw new RuntimeInterruptedException();
        }
        double depScore = depScoreTree(candidate.object());
        ScoredObject x = new ScoredObject<>(candidate.object(), candidate.score() + depScore);
        goodParses.add(x);
      }
      nGoodTrees = goodParses.asSortedList();
    }
    return ! nGoodTrees.isEmpty();
  }

  /** Get the exact k best parses for the sentence.
   *
   *  @param k The number of best parses to return
   *  @return The exact k best parses for the sentence, with
   *         each accompanied by its score (typically a
   *         negative log probability).
   */
  public List> getKBestParses(int k) {
    throw new UnsupportedOperationException();
  }


  /** Get a complete set of the maximally scoring parses for a sentence,
   *  rather than one chosen at random.  This set may be of size 1 or larger.
   *
   *  @return All the equal best parses for a sentence, with each
   *         accompanied by its score
   */
  public List> getBestParses() {
    throw new UnsupportedOperationException();
  }

  /** Get k parse samples for the sentence.  It is expected that the
   *  parses are sampled based on their relative probability.
   *
   *  @param k The number of sampled parses to return
   *  @return A list of k parse samples for the sentence, with
   *         each accompanied by its score
   */
  public List> getKSampledParses(int k) {
    throw new UnsupportedOperationException();
  }


  FastFactoredParser(ExhaustivePCFGParser pparser, MLEDependencyGrammar dg, Options op, int numToFind, Index wordIndex, Index tagIndex) {
    this(pparser, dg, op, numToFind, new NullGrammarProjection(null, null), wordIndex, tagIndex);
  }

  FastFactoredParser(ExhaustivePCFGParser pparser, MLEDependencyGrammar dg, Options op, int numToFind, GrammarProjection projection, Index wordIndex, Index tagIndex) {
    this.pparser = pparser;
    this.projection = projection;
    this.dg = dg;
    this.op = op;
    this.numToFind = numToFind;
    this.wordIndex = wordIndex;
    this.tagIndex = tagIndex;
  }

} // end class FastFactoredParser