/**
* Generate the training features from the CoNLL input file.
*
* @return Dataset of feature vectors
* @throws Exception
*/
public GeneralDataset<String, String> generateFeatureVectors(Properties props) throws Exception {
GeneralDataset<String, String> dataset = new Dataset<>();
Dictionaries dict = new Dictionaries(props);
MentionExtractor mentionExtractor = new CoNLLMentionExtractor(dict, props, new Semantics(dict));
Document document;
while ((document = mentionExtractor.nextDoc()) != null) {
setTokenIndices(document);
document.extractGoldCorefClusters();
Map<Integer, CorefCluster> entities = document.goldCorefClusters;
// Generate features for coreferent mentions with class label 1
for (CorefCluster entity : entities.values()) {
for (Mention mention : entity.getCorefMentions()) {
// Ignore verbal mentions
if (mention.headWord.tag().startsWith("V")) continue;
IndexedWord head = mention.dependency.getNodeByIndexSafe(mention.headWord.index());
if (head == null) continue;
ArrayList<String> feats = mention.getSingletonFeatures(dict);
dataset.add(new BasicDatum<>(feats, "1"));
}
}
// Generate features for singletons with class label 0
ArrayList<CoreLabel> gold_heads = new ArrayList<>();
for (Mention gold_men : document.allGoldMentions.values()) {
gold_heads.add(gold_men.headWord);
}
for (Mention predicted_men : document.allPredictedMentions.values()) {
SemanticGraph dep = predicted_men.dependency;
IndexedWord head = dep.getNodeByIndexSafe(predicted_men.headWord.index());
if (head == null) continue;
// Ignore verbal mentions
if (predicted_men.headWord.tag().startsWith("V")) continue;
// If the mention is in the gold set, it is not a singleton and thus ignore
if (gold_heads.contains(predicted_men.headWord)) continue;
dataset.add(new BasicDatum<>(predicted_men.getSingletonFeatures(dict), "0"));
}
}
dataset.summaryStatistics();
return dataset;
}
public void resetDocs() {
super.resetDocs();
reader.reset();
}
@Override
public void resetDocs() {
super.resetDocs();
currentOffset = 0;
}