public virtual void RunCoref(Document document)
{
IDictionary <Pair <int, int>, bool> mentionPairs = CorefUtils.GetUnlabeledMentionPairs(document);
if (mentionPairs.Count == 0)
{
return;
}
Compressor <string> compressor = new Compressor <string>();
DocumentExamples examples = extractor.Extract(0, document, mentionPairs, compressor);
ICounter <Pair <int, int> > classificationScores = new ClassicCounter <Pair <int, int> >();
ICounter <Pair <int, int> > rankingScores = new ClassicCounter <Pair <int, int> >();
ICounter <int> anaphoricityScores = new ClassicCounter <int>();
foreach (Example example in examples.examples)
{
CorefUtils.CheckForInterrupt();
Pair <int, int> mentionPair = new Pair <int, int>(example.mentionId1, example.mentionId2);
classificationScores.IncrementCount(mentionPair, classificationModel.Predict(example, examples.mentionFeatures, compressor));
rankingScores.IncrementCount(mentionPair, rankingModel.Predict(example, examples.mentionFeatures, compressor));
if (!anaphoricityScores.ContainsKey(example.mentionId2))
{
anaphoricityScores.IncrementCount(example.mentionId2, anaphoricityModel.Predict(new Example(example, false), examples.mentionFeatures, compressor));
}
}
ClustererDataLoader.ClustererDoc doc = new ClustererDataLoader.ClustererDoc(0, classificationScores, rankingScores, anaphoricityScores, mentionPairs, null, document.predictedMentionsByID.Stream().Collect(Collectors.ToMap(null, null)));
foreach (Pair <int, int> mentionPair_1 in clusterer.GetClusterMerges(doc))
{
CorefUtils.MergeCoreferenceClusters(mentionPair_1, document);
}
}
public virtual void RunCoref(Document document)
{
Compressor <string> compressor = new Compressor <string>();
if (Thread.Interrupted())
{
// Allow interrupting
throw new RuntimeInterruptedException();
}
IDictionary <Pair <int, int>, bool> pairs = new Dictionary <Pair <int, int>, bool>();
foreach (KeyValuePair <int, IList <int> > e in CorefUtils.HeuristicFilter(CorefUtils.GetSortedMentions(document), maxMentionDistance, maxMentionDistanceWithStringMatch))
{
foreach (int m1 in e.Value)
{
pairs[new Pair <int, int>(m1, e.Key)] = true;
}
}
DocumentExamples examples = extractor.Extract(0, document, pairs, compressor);
ICounter <Pair <int, int> > pairwiseScores = new ClassicCounter <Pair <int, int> >();
foreach (Example mentionPair in examples.examples)
{
if (Thread.Interrupted())
{
// Allow interrupting
throw new RuntimeInterruptedException();
}
pairwiseScores.IncrementCount(new Pair <int, int>(mentionPair.mentionId1, mentionPair.mentionId2), classifier.Predict(mentionPair, examples.mentionFeatures, compressor));
}
IList <Pair <int, int> > mentionPairs = new List <Pair <int, int> >(pairwiseScores.KeySet());
mentionPairs.Sort(null);
ICollection <int> seenAnaphors = new HashSet <int>();
foreach (Pair <int, int> pair in mentionPairs)
{
if (seenAnaphors.Contains(pair.second))
{
continue;
}
if (Thread.Interrupted())
{
// Allow interrupting
throw new RuntimeInterruptedException();
}
seenAnaphors.Add(pair.second);
Dictionaries.MentionType mt1 = document.predictedMentionsByID[pair.first].mentionType;
Dictionaries.MentionType mt2 = document.predictedMentionsByID[pair.second].mentionType;
if (pairwiseScores.GetCount(pair) > thresholds[new Pair <bool, bool>(mt1 == Dictionaries.MentionType.Pronominal, mt2 == Dictionaries.MentionType.Pronominal)])
{
CorefUtils.MergeCoreferenceClusters(pair, document);
}
}
}
public static IList <Pair <Example, IDictionary <int, CompressedFeatureVector> > > GetExamples(IList <DocumentExamples> documents)
{
IList <Pair <Example, IDictionary <int, CompressedFeatureVector> > > examples = new List <Pair <Example, IDictionary <int, CompressedFeatureVector> > >();
while (!documents.IsEmpty())
{
DocumentExamples doc = documents.Remove(documents.Count - 1);
IDictionary <int, CompressedFeatureVector> mentionFeatures = doc.mentionFeatures;
foreach (Example e in doc.examples)
{
examples.Add(new Pair <Example, IDictionary <int, CompressedFeatureVector> >(e, mentionFeatures));
}
}
return(examples);
}
public static IList <Pair <Example, IDictionary <int, CompressedFeatureVector> > > GetAnaphoricityExamples(IList <DocumentExamples> documents)
{
int p = 0;
int t = 0;
IList <Pair <Example, IDictionary <int, CompressedFeatureVector> > > examples = new List <Pair <Example, IDictionary <int, CompressedFeatureVector> > >();
while (!documents.IsEmpty())
{
DocumentExamples doc = documents.Remove(documents.Count - 1);
IDictionary <int, bool> areAnaphoric = new Dictionary <int, bool>();
foreach (Example e in doc.examples)
{
bool isAnaphoric = areAnaphoric[e.mentionId2];
if (isAnaphoric == null)
{
areAnaphoric[e.mentionId2] = false;
}
if (e.label == 1)
{
areAnaphoric[e.mentionId2] = true;
}
}
foreach (KeyValuePair <int, bool> e_1 in areAnaphoric)
{
if (e_1.Value)
{
p++;
}
t++;
}
foreach (Example e_2 in doc.examples)
{
bool isAnaphoric = areAnaphoric[e_2.mentionId2];
if (isAnaphoric != null)
{
Sharpen.Collections.Remove(areAnaphoric, e_2.mentionId2);
examples.Add(new Pair <Example, IDictionary <int, CompressedFeatureVector> >(new Example(e_2, isAnaphoric), doc.mentionFeatures));
}
}
}
Redwood.Log("scoref-train", "Num anaphoricity examples " + p + " positive, " + t + " total");
return(examples);
}
