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<String>. Change defaultSentence() to be List<HasWord>
protected static final boolean VERBOSE = false;
protected ExhaustivePCFGParser pparser;
protected GrammarProjection projection;
protected MLEDependencyGrammar dg;
protected Options op;
private int numToFind;
private final Index<String> wordIndex;
private final Index<String> 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<ScoredObject<Tree>> 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<ScoredObject<Tree>> 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<IntDependency> 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<? extends HasWord> words) {
nGoodTrees.clear();
int numParsesToConsider = numToFind * op.testOptions.fastFactoredCandidateMultiplier + op.testOptions.fastFactoredCandidateAddend;
if (pparser.hasParse()) {
List<ScoredObject<Tree>> pcfgBest = pparser.getKBestParses(numParsesToConsider);
Beam<ScoredObject<Tree>> goodParses = new Beam<>(numToFind);
for (ScoredObject<Tree> candidate : pcfgBest) {
if (Thread.interrupted()) {
throw new RuntimeInterruptedException();
}
double depScore = depScoreTree(candidate.object());
ScoredObject<Tree> 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<ScoredObject<Tree>> 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<ScoredObject<Tree>> 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<ScoredObject<Tree>> getKSampledParses(int k) {
throw new UnsupportedOperationException();
}
FastFactoredParser(ExhaustivePCFGParser pparser, MLEDependencyGrammar dg, Options op, int numToFind, Index<String> wordIndex, Index<String> tagIndex) {
this(pparser, dg, op, numToFind, new NullGrammarProjection(null, null), wordIndex, tagIndex);
}
FastFactoredParser(ExhaustivePCFGParser pparser, MLEDependencyGrammar dg, Options op, int numToFind, GrammarProjection projection, Index<String> wordIndex, Index<String> 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