private static ICounter <string> GetFeatures(ClustererDataLoader.ClustererDoc doc, Clusterer.Cluster c1, Clusterer.Cluster c2, Clusterer.GlobalFeatures gf)
{
Clusterer.MergeKey key = new Clusterer.MergeKey(c1, c2, gf.currentIndex);
CompressedFeatureVector cfv = featuresCache[key];
ICounter <string> features = cfv == null ? null : compressor.Uncompress(cfv);
if (features != null)
{
featuresCacheHits += isTraining;
return(features);
}
featuresCacheMisses += isTraining;
features = new ClassicCounter <string>();
if (gf.anaphorSeen)
{
features.IncrementCount("anaphorSeen");
}
features.IncrementCount("docSize", gf.docSize);
features.IncrementCount("percentComplete", gf.currentIndex / (double)gf.size);
features.IncrementCount("bias", 1.0);
int earliest1 = EarliestMention(c1, doc);
int earliest2 = EarliestMention(c2, doc);
if (doc.mentionIndices[earliest1] > doc.mentionIndices[earliest2])
{
int tmp = earliest1;
earliest1 = earliest2;
earliest2 = tmp;
}
features.IncrementCount("anaphoricity", doc.anaphoricityScores.GetCount(earliest2));
if (c1.mentions.Count == 1 && c2.mentions.Count == 1)
{
Pair <int, int> mentionPair = new Pair <int, int>(c1.mentions[0], c2.mentions[0]);
features.AddAll(AddSuffix(GetFeatures(doc, mentionPair, doc.classificationScores), "-classification"));
features.AddAll(AddSuffix(GetFeatures(doc, mentionPair, doc.rankingScores), "-ranking"));
features = AddSuffix(features, "-single");
}
else
{
IList <Pair <int, int> > between = new List <Pair <int, int> >();
foreach (int m1 in c1.mentions)
{
foreach (int m2 in c2.mentions)
{
between.Add(new Pair <int, int>(m1, m2));
}
}
features.AddAll(AddSuffix(GetFeatures(doc, between, doc.classificationScores), "-classification"));
features.AddAll(AddSuffix(GetFeatures(doc, between, doc.rankingScores), "-ranking"));
}
featuresCache[key] = compressor.Compress(features);
return(features);
}
static ChineseQuantifiableEntityNormalizer()
{
//Entity types that are quantifiable
// used by money
// used by money
// Patterns we need
// TODO (yuhao): here we are not considering 1) negative numbers, 2) Chinese traditional characters
// This is the all-literal-number-characters sequence, excluding unit characters like 十 or 万
// The decimal part of a float number should be exactly literal number sequence without units
// Used by quantity modifiers
// All the tags we need
// static initialization of useful properties
quantifiable = Generics.NewHashSet();
quantifiable.Add(NumberTag);
quantifiable.Add(DateTag);
quantifiable.Add(TimeTag);
quantifiable.Add(MoneyTag);
quantifiable.Add(PercentTag);
quantifiable.Add(OrdinalTag);
quantityUnitToValues = new ClassicCounter <string>();
quantityUnitToValues.SetCount("十", 10.0);
quantityUnitToValues.SetCount("百", 100.0);
quantityUnitToValues.SetCount("千", 1000.0);
quantityUnitToValues.SetCount("万", 10000.0);
quantityUnitToValues.SetCount("亿", 100000000.0);
wordsToValues = new ClassicCounter <string>();
wordsToValues.SetCount("零", 0.0);
wordsToValues.SetCount("〇", 0.0);
wordsToValues.SetCount("一", 1.0);
wordsToValues.SetCount("二", 2.0);
wordsToValues.SetCount("两", 2.0);
wordsToValues.SetCount("三", 3.0);
wordsToValues.SetCount("四", 4.0);
wordsToValues.SetCount("五", 5.0);
wordsToValues.SetCount("六", 6.0);
wordsToValues.SetCount("七", 7.0);
wordsToValues.SetCount("八", 8.0);
wordsToValues.SetCount("九", 9.0);
wordsToValues.AddAll(quantityUnitToValues);
// all units are also quantifiable individually
multiCharCurrencyWords = Generics.NewHashMap();
multiCharCurrencyWords["美元"] = '$';
multiCharCurrencyWords["美分"] = '$';
multiCharCurrencyWords["英镑"] = '£';
multiCharCurrencyWords["先令"] = '£';
multiCharCurrencyWords["便士"] = '£';
multiCharCurrencyWords["欧元"] = '€';
multiCharCurrencyWords["日元"] = '¥';
multiCharCurrencyWords["韩元"] = '₩';
oneCharCurrencyWords = Generics.NewHashMap();
oneCharCurrencyWords["刀"] = '$';
oneCharCurrencyWords["镑"] = '£';
oneCharCurrencyWords["元"] = '元';
// We follow the tradition in English to use 元 instead of ¥ for RMB
// For all other currency, we use default currency symbol $
yearModifiers = Generics.NewHashMap();
yearModifiers["前"] = -2;
yearModifiers["去"] = -1;
yearModifiers["上"] = -1;
yearModifiers["今"] = 0;
yearModifiers["同"] = 0;
yearModifiers["此"] = 0;
yearModifiers["该"] = 0;
yearModifiers["本"] = 0;
yearModifiers["明"] = 1;
yearModifiers["来"] = 1;
yearModifiers["下"] = 1;
yearModifiers["后"] = 2;
monthDayModifiers = Generics.NewHashMap();
monthDayModifiers["昨"] = -1;
monthDayModifiers["上"] = -1;
monthDayModifiers["今"] = 0;
monthDayModifiers["同"] = 0;
monthDayModifiers["此"] = 0;
monthDayModifiers["该"] = 0;
monthDayModifiers["本"] = 0;
monthDayModifiers["来"] = 1;
monthDayModifiers["明"] = 1;
monthDayModifiers["下"] = 1;
fullDigitToHalfDigit = Generics.NewHashMap();
fullDigitToHalfDigit["１"] = "1";
fullDigitToHalfDigit["２"] = "2";
fullDigitToHalfDigit["３"] = "3";
fullDigitToHalfDigit["４"] = "4";
fullDigitToHalfDigit["５"] = "5";
fullDigitToHalfDigit["６"] = "6";
fullDigitToHalfDigit["７"] = "7";
fullDigitToHalfDigit["８"] = "8";
fullDigitToHalfDigit["９"] = "9";
fullDigitToHalfDigit["０"] = "0";
}
public virtual ICounter <string> GetFeatures(Example example, IDictionary <int, CompressedFeatureVector> mentionFeatures, Compressor <string> compressor)
{
ICounter <string> features = new ClassicCounter <string>();
ICounter <string> pairFeatures = new ClassicCounter <string>();
ICounter <string> features1 = new ClassicCounter <string>();
ICounter <string> features2 = compressor.Uncompress(mentionFeatures[example.mentionId2]);
if (!example.IsNewLink())
{
System.Diagnostics.Debug.Assert((!anaphoricityClassifier));
pairFeatures = compressor.Uncompress(example.pairwiseFeatures);
features1 = compressor.Uncompress(mentionFeatures[example.mentionId1]);
}
else
{
features2.IncrementCount("bias");
}
if (!disallowedPrefixes.IsEmpty())
{
features1 = FilterOut(features1, disallowedPrefixes);
features2 = FilterOut(features2, disallowedPrefixes);
pairFeatures = FilterOut(pairFeatures, disallowedPrefixes);
}
IList <string> ids1 = example.IsNewLink() ? new List <string>() : Identifiers(features1, example.mentionType1);
IList <string> ids2 = Identifiers(features2, example.mentionType2);
features.AddAll(pairFeatures);
foreach (string id1 in ids1)
{
foreach (string id2 in ids2)
{
if (pairConjunctions.Contains(MetaFeatureExtractor.PairConjunction.First))
{
features.AddAll(GetConjunction(pairFeatures, "_m1=" + id1));
}
if (pairConjunctions.Contains(MetaFeatureExtractor.PairConjunction.Last))
{
features.AddAll(GetConjunction(pairFeatures, "_m2=" + id2));
}
if (pairConjunctions.Contains(MetaFeatureExtractor.PairConjunction.Both))
{
features.AddAll(GetConjunction(pairFeatures, "_ms=" + id1 + "_" + id2));
}
if (singleConjunctions.Contains(MetaFeatureExtractor.SingleConjunction.Index))
{
features.AddAll(GetConjunction(features1, "_1"));
features.AddAll(GetConjunction(features2, "_2"));
}
if (singleConjunctions.Contains(MetaFeatureExtractor.SingleConjunction.IndexCurrent))
{
features.AddAll(GetConjunction(features1, "_1" + "_m=" + id1));
features.AddAll(GetConjunction(features2, "_2" + "_m=" + id2));
}
if (singleConjunctions.Contains(MetaFeatureExtractor.SingleConjunction.IndexLast))
{
features.AddAll(GetConjunction(features1, "_1" + "_m2=" + id2));
features.AddAll(GetConjunction(features2, "_2" + "_m2=" + id2));
}
if (singleConjunctions.Contains(MetaFeatureExtractor.SingleConjunction.IndexOther))
{
features.AddAll(GetConjunction(features1, "_1" + "_m=" + id2));
features.AddAll(GetConjunction(features2, "_2" + "_m=" + id1));
}
if (singleConjunctions.Contains(MetaFeatureExtractor.SingleConjunction.IndexBoth))
{
features.AddAll(GetConjunction(features1, "_1" + "_ms=" + id1 + "_" + id2));
features.AddAll(GetConjunction(features2, "_2" + "_ms=" + id1 + "_" + id2));
}
}
}
if (example.IsNewLink())
{
features.AddAll(features2);
features.AddAll(GetConjunction(features2, "_m=" + ids2[0]));
ICounter <string> newFeatures = new ClassicCounter <string>();
foreach (KeyValuePair <string, double> e in features.EntrySet())
{
newFeatures.IncrementCount(e.Key + "_NEW", e.Value);
}
features = newFeatures;
}
return(features);
}
