public List<ClusteredMention> runCoreference(Document doc) {
// --Overhead
startTrack("Testing " + doc.id);
// (variables)
List<ClusteredMention> rtn = new ArrayList<ClusteredMention>(doc.getMentions().size());
List<Mention> mentions = doc.getMentions();
int singletons = 0;
// --Run Classifier
for (int i = 0; i < mentions.size(); i++) {
// (variables)
Mention onPrix = mentions.get(i);
int coreferentWith = -1;
// (get mention it is coreferent with)
for (int j = i - 1; j >= 0; j--) {
ClusteredMention cand = rtn.get(j);
boolean coreferent =
classifier.classOf(
new RVFDatum<Boolean, Feature>(extractor.extractFeatures(Pair.make(onPrix, cand))));
if (coreferent) {
coreferentWith = j;
break;
}
}
if (coreferentWith < 0) {
singletons += 1;
rtn.add(onPrix.markSingleton());
} else {
// log("Mention " + onPrix + " coreferent with " + mentions.get(coreferentWith));
rtn.add(onPrix.markCoreferent(rtn.get(coreferentWith)));
}
}
// log("" + singletons + " singletons");
// --Return
endTrack("Testing " + doc.id);
return rtn;
}
private <E> Feature feature(
Class<E> clazz, Pair<Mention, ClusteredMention> input, Option<Double> count) {
// --Variables
Mention onPrix =
input.getFirst(); // the first mention (referred to as m_i in the handout)
Mention candidate =
input.getSecond().mention; // the second mention (referred to as m_j in the handout)
Entity candidateCluster =
input.getSecond().entity; // the cluster containing the second mention
// --Features:w
if (clazz.equals(Feature.ExactMatch.class)) {
// (exact string match)
return new Feature.ExactMatch(onPrix.gloss().equals(candidate.gloss()));
} else if (clazz.equals(Feature.SentenceDist.class)) {
return new Feature.SentenceDist(
Math.abs(
onPrix.doc.indexOfMention(onPrix) - candidate.doc.indexOfMention(candidate)));
} else if (clazz.equals(Feature.MentionDist.class)) {
return new Feature.MentionDist(
Math.abs(
onPrix.doc.indexOfSentence(onPrix.sentence)
- candidate.doc.indexOfSentence(candidate.sentence)));
} else if (clazz.equals(Feature.EitherHeadWordPronoun.class)) {
return new Feature.EitherHeadWordPronoun(
Pronoun.isSomePronoun(onPrix.gloss()) || Pronoun.isSomePronoun(candidate.gloss()));
} else if (clazz.equals(Feature.CandidateNERTag.class)) {
return new Feature.CandidateNERTag(candidate.headToken().nerTag());
} else if (clazz.equals(Feature.CandidateSpeaker.class)) {
return new Feature.CandidateSpeaker(candidate.headToken().speaker());
} else if (clazz.equals(Feature.FixedSpeaker.class)) {
return new Feature.FixedSpeaker(onPrix.headToken().speaker());
} else if (clazz.equals(Feature.HeadWordMatch.class)) {
return new Feature.HeadWordMatch(onPrix.equals(candidate.headWord()));
} else if (clazz.equals(Feature.HeadWordLemmaMatch.class)) {
return new Feature.HeadWordLemmaMatch(
onPrix.headToken().lemma().equals(candidate.headToken().lemma()));
} else if (clazz.equals(Feature.FixedNERTag.class)) {
return new Feature.FixedNERTag(onPrix.headToken().nerTag());
} else if (clazz.equals(Feature.SpeakerMatch.class)) {
return new Feature.SpeakerMatch(
candidate.headToken().speaker().equals(onPrix.headToken().speaker()));
} else if (clazz.equals(Feature.NERTagMatch.class)) {
return new Feature.NERTagMatch(
candidate.headToken().nerTag().equals(onPrix.headToken().nerTag()));
} else if (clazz.equals(Feature.CandidatePOSTag.class)) {
return new Feature.CandidatePOSTag(candidate.headToken().posTag());
} else if (clazz.equals(Feature.FixedPOSTag.class)) {
return new Feature.FixedPOSTag(onPrix.headToken().posTag());
} else if (clazz.equals(Feature.GenderMatch.class)) {
Pair<Boolean, Boolean> match = Util.haveGenderAndAreSameGender(onPrix, candidate);
boolean finalMatch = (!match.getFirst() || match.getSecond());
return new Feature.GenderMatch(finalMatch);
} else if (clazz.equals(Feature.NumberMatch.class)) {
Pair<Boolean, Boolean> match = Util.haveNumberAndAreSameNumber(onPrix, candidate);
boolean finalMatch = (!match.getFirst() || match.getSecond());
return new Feature.NumberMatch(finalMatch);
}
// } else if(clazz.equals(Feature.NewFeature.class) {
/*
* TODO: Add features to return for specific classes. Implement calculating values of features here.
*/
else {
throw new IllegalArgumentException("Unregistered feature: " + clazz);
}
}
public void train(Collection<Pair<Document, List<Entity>>> trainingData) {
startTrack("Training");
// --Variables
RVFDataset<Boolean, Feature> dataset = new RVFDataset<Boolean, Feature>();
LinearClassifierFactory<Boolean, Feature> fact =
new LinearClassifierFactory<Boolean, Feature>();
// --Feature Extraction
startTrack("Feature Extraction");
for (Pair<Document, List<Entity>> datum : trainingData) {
// (document variables)
Document doc = datum.getFirst();
List<Entity> goldClusters = datum.getSecond();
List<Mention> mentions = doc.getMentions();
Map<Mention, Entity> goldEntities = Entity.mentionToEntityMap(goldClusters);
startTrack("Document " + doc.id);
// (for each mention...)
for (int i = 0; i < mentions.size(); i++) {
// (get the mention and its cluster)
Mention onPrix = mentions.get(i);
Entity source = goldEntities.get(onPrix);
if (source == null) {
throw new IllegalArgumentException("Mention has no gold entity: " + onPrix);
}
// (for each previous mention...)
int oldSize = dataset.size();
for (int j = i - 1; j >= 0; j--) {
// (get previous mention and its cluster)
Mention cand = mentions.get(j);
Entity target = goldEntities.get(cand);
if (target == null) {
throw new IllegalArgumentException("Mention has no gold entity: " + cand);
}
// (extract features)
Counter<Feature> feats =
extractor.extractFeatures(Pair.make(onPrix, cand.markCoreferent(target)));
// (add datum)
dataset.add(new RVFDatum<Boolean, Feature>(feats, target == source));
// (stop if
if (target == source) {
break;
}
}
// logf("Mention %s (%d datums)", onPrix.toString(), dataset.size() - oldSize);
}
endTrack("Document " + doc.id);
}
endTrack("Feature Extraction");
// --Train Classifier
startTrack("Minimizer");
this.classifier = fact.trainClassifier(dataset);
endTrack("Minimizer");
// --Dump Weights
startTrack("Features");
// (get labels to print)
Set<Boolean> labels = new HashSet<Boolean>();
labels.add(true);
// (print features)
for (Triple<Feature, Boolean, Double> featureInfo :
this.classifier.getTopFeatures(labels, 0.0, true, 100, true)) {
Feature feature = featureInfo.first();
Boolean label = featureInfo.second();
Double magnitude = featureInfo.third();
// log(FORCE,new DecimalFormat("0.000").format(magnitude) + " [" + label + "] " + feature);
}
end_Track("Features");
endTrack("Training");
}
