use of edu.stanford.nlp.ling.CoreLabel in project CoreNLP by stanfordnlp.
the class TrueCasingForNISTDocumentReaderAndWriter method main.
/**
* for test only
**/
public static void main(String[] args) throws IOException {
Reader reader = new BufferedReader(new FileReader(args[0]));
TrueCasingForNISTDocumentReaderAndWriter raw = new TrueCasingForNISTDocumentReaderAndWriter();
raw.init(null);
for (Iterator<List<CoreLabel>> it = raw.getIterator(reader); it.hasNext(); ) {
List<CoreLabel> l = it.next();
for (CoreLabel cl : l) {
System.out.println(cl);
}
System.out.println("========================================");
}
}
use of edu.stanford.nlp.ling.CoreLabel in project CoreNLP by stanfordnlp.
the class ArrayCoreMapTest method testCoreLabelSetWordBehavior.
public void testCoreLabelSetWordBehavior() {
CoreLabel foo = new CoreLabel();
foo.set(CoreAnnotations.TextAnnotation.class, "foo");
foo.set(CoreAnnotations.PartOfSpeechAnnotation.class, "B");
foo.set(CoreAnnotations.LemmaAnnotation.class, "fool");
// Lemma gets removed with word
ArrayCoreMap copy = new ArrayCoreMap(foo);
assertEquals(copy, foo);
foo.setWord("foo");
// same word set
assertEquals(copy, foo);
foo.setWord("bar");
// lemma removed
assertFalse(copy.equals(foo));
foo.setWord("foo");
// still removed
assertFalse(copy.equals(foo));
foo.set(CoreAnnotations.LemmaAnnotation.class, "fool");
// back to normal
assertEquals(copy, foo);
// Hash code is consistent
int hashCode = foo.hashCode();
assertEquals(copy.hashCode(), hashCode);
foo.setWord("bar");
assertFalse(hashCode == foo.hashCode());
foo.setWord("foo");
assertFalse(hashCode == foo.hashCode());
// Hash code doesn't care between a value of null and the key not existing
assertTrue(foo.lemma() == null);
int lemmalessHashCode = foo.hashCode();
foo.remove(CoreAnnotations.LemmaAnnotation.class);
assertEquals(lemmalessHashCode, foo.hashCode());
foo.setLemma(null);
assertEquals(lemmalessHashCode, foo.hashCode());
foo.setLemma("fool");
assertEquals(hashCode, foo.hashCode());
// Check equals
foo.setWord("bar");
foo.setWord("foo");
ArrayCoreMap nulledCopy = new ArrayCoreMap(foo);
assertEquals(nulledCopy, foo);
foo.remove(CoreAnnotations.LemmaAnnotation.class);
assertEquals(nulledCopy, foo);
}
use of edu.stanford.nlp.ling.CoreLabel in project CoreNLP by stanfordnlp.
the class RFSieve method extractDatum.
public static RVFDatum<Boolean, String> extractDatum(Mention m, Mention candidate, Document document, int mentionDist, Dictionaries dict, Properties props, String sievename) {
try {
boolean label = (document.goldMentions == null) ? false : document.isCoref(m, candidate);
Counter<String> features = new ClassicCounter<>();
CorefCluster mC = document.corefClusters.get(m.corefClusterID);
CorefCluster aC = document.corefClusters.get(candidate.corefClusterID);
CoreLabel mFirst = m.sentenceWords.get(m.startIndex);
CoreLabel mLast = m.sentenceWords.get(m.endIndex - 1);
CoreLabel mPreceding = (m.startIndex > 0) ? m.sentenceWords.get(m.startIndex - 1) : null;
CoreLabel mFollowing = (m.endIndex < m.sentenceWords.size()) ? m.sentenceWords.get(m.endIndex) : null;
CoreLabel aFirst = candidate.sentenceWords.get(candidate.startIndex);
CoreLabel aLast = candidate.sentenceWords.get(candidate.endIndex - 1);
CoreLabel aPreceding = (candidate.startIndex > 0) ? candidate.sentenceWords.get(candidate.startIndex - 1) : null;
CoreLabel aFollowing = (candidate.endIndex < candidate.sentenceWords.size()) ? candidate.sentenceWords.get(candidate.endIndex) : null;
////////////////////////////////////////////////////////////////////////////////
if (HybridCorefProperties.useBasicFeatures(props, sievename)) {
int sentDist = m.sentNum - candidate.sentNum;
features.incrementCount("SENTDIST", sentDist);
features.incrementCount("MENTIONDIST", mentionDist);
int minSentDist = sentDist;
for (Mention a : aC.corefMentions) {
minSentDist = Math.min(minSentDist, Math.abs(m.sentNum - a.sentNum));
}
features.incrementCount("MINSENTDIST", minSentDist);
// When they are in the same sentence, divides a sentence into clauses and add such feature
if (CorefProperties.useConstituencyParse(props)) {
if (m.sentNum == candidate.sentNum) {
int clauseCount = 0;
Tree tree = m.contextParseTree;
Tree current = m.mentionSubTree;
while (true) {
current = current.ancestor(1, tree);
if (current.label().value().startsWith("S")) {
clauseCount++;
}
if (current.dominates(candidate.mentionSubTree))
break;
if (current.label().value().equals("ROOT") || current.ancestor(1, tree) == null)
break;
}
features.incrementCount("CLAUSECOUNT", clauseCount);
}
}
if (document.docType == DocType.CONVERSATION)
features.incrementCount("B-DOCTYPE-" + document.docType);
if (m.headWord.get(SpeakerAnnotation.class).equalsIgnoreCase("PER0")) {
features.incrementCount("B-SPEAKER-PER0");
}
if (document.docInfo != null && document.docInfo.containsKey("DOC_ID")) {
features.incrementCount("B-DOCSOURCE-" + document.docInfo.get("DOC_ID").split("/")[1]);
}
features.incrementCount("M-LENGTH", m.originalSpan.size());
features.incrementCount("A-LENGTH", candidate.originalSpan.size());
if (m.originalSpan.size() < candidate.originalSpan.size())
features.incrementCount("B-A-ISLONGER");
features.incrementCount("A-SIZE", aC.getCorefMentions().size());
features.incrementCount("M-SIZE", mC.getCorefMentions().size());
String antRole = "A-NOROLE";
String mRole = "M-NOROLE";
if (m.isSubject)
mRole = "M-SUBJ";
if (m.isDirectObject)
mRole = "M-DOBJ";
if (m.isIndirectObject)
mRole = "M-IOBJ";
if (m.isPrepositionObject)
mRole = "M-POBJ";
if (candidate.isSubject)
antRole = "A-SUBJ";
if (candidate.isDirectObject)
antRole = "A-DOBJ";
if (candidate.isIndirectObject)
antRole = "A-IOBJ";
if (candidate.isPrepositionObject)
antRole = "A-POBJ";
features.incrementCount("B-" + mRole);
features.incrementCount("B-" + antRole);
features.incrementCount("B-" + antRole + "-" + mRole);
if (HybridCorefProperties.combineObjectRoles(props, sievename)) {
// combine all objects
if (m.isDirectObject || m.isIndirectObject || m.isPrepositionObject || candidate.isDirectObject || candidate.isIndirectObject || candidate.isPrepositionObject) {
if (m.isDirectObject || m.isIndirectObject || m.isPrepositionObject) {
mRole = "M-OBJ";
features.incrementCount("B-M-OBJ");
}
if (candidate.isDirectObject || candidate.isIndirectObject || candidate.isPrepositionObject) {
antRole = "A-OBJ";
features.incrementCount("B-A-OBJ");
}
features.incrementCount("B-" + antRole + "-" + mRole);
}
}
if (mFirst.word().toLowerCase().matches("a|an")) {
features.incrementCount("B-M-START-WITH-INDEFINITE");
}
if (aFirst.word().toLowerCase().matches("a|an")) {
features.incrementCount("B-A-START-WITH-INDEFINITE");
}
if (mFirst.word().equalsIgnoreCase("the")) {
features.incrementCount("B-M-START-WITH-DEFINITE");
}
if (aFirst.word().equalsIgnoreCase("the")) {
features.incrementCount("B-A-START-WITH-DEFINITE");
}
if (dict.indefinitePronouns.contains(m.lowercaseNormalizedSpanString())) {
features.incrementCount("B-M-INDEFINITE-PRONOUN");
}
if (dict.indefinitePronouns.contains(candidate.lowercaseNormalizedSpanString())) {
features.incrementCount("B-A-INDEFINITE-PRONOUN");
}
if (dict.indefinitePronouns.contains(mFirst.word().toLowerCase())) {
features.incrementCount("B-M-INDEFINITE-ADJ");
}
if (dict.indefinitePronouns.contains(aFirst.word().toLowerCase())) {
features.incrementCount("B-A-INDEFINITE-ADJ");
}
if (dict.reflexivePronouns.contains(m.headString)) {
features.incrementCount("B-M-REFLEXIVE");
}
if (dict.reflexivePronouns.contains(candidate.headString)) {
features.incrementCount("B-A-REFLEXIVE");
}
if (m.headIndex == m.endIndex - 1)
features.incrementCount("B-M-HEADEND");
if (m.headIndex < m.endIndex - 1) {
CoreLabel headnext = m.sentenceWords.get(m.headIndex + 1);
if (headnext.word().matches("that|,") || headnext.tag().startsWith("W")) {
features.incrementCount("B-M-HASPOSTPHRASE");
if (mFirst.tag().equals("DT") && mFirst.word().toLowerCase().matches("the|this|these|those"))
features.incrementCount("B-M-THE-HASPOSTPHRASE");
else if (mFirst.word().toLowerCase().matches("a|an"))
features.incrementCount("B-M-INDEFINITE-HASPOSTPHRASE");
}
}
// shape feature from Bjorkelund & Kuhn
StringBuilder sb = new StringBuilder();
List<Mention> sortedMentions = new ArrayList<>(aC.corefMentions.size());
sortedMentions.addAll(aC.corefMentions);
Collections.sort(sortedMentions, new CorefChain.MentionComparator());
for (Mention a : sortedMentions) {
sb.append(a.mentionType).append("-");
}
features.incrementCount("B-A-SHAPE-" + sb.toString());
sb = new StringBuilder();
sortedMentions = new ArrayList<>(mC.corefMentions.size());
sortedMentions.addAll(mC.corefMentions);
Collections.sort(sortedMentions, new CorefChain.MentionComparator());
for (Mention men : sortedMentions) {
sb.append(men.mentionType).append("-");
}
features.incrementCount("B-M-SHAPE-" + sb.toString());
if (CorefProperties.useConstituencyParse(props)) {
sb = new StringBuilder();
Tree mTree = m.contextParseTree;
Tree mHead = mTree.getLeaves().get(m.headIndex).ancestor(1, mTree);
for (Tree node : mTree.pathNodeToNode(mHead, mTree)) {
sb.append(node.value()).append("-");
if (node.value().equals("S"))
break;
}
features.incrementCount("B-M-SYNPATH-" + sb.toString());
sb = new StringBuilder();
Tree aTree = candidate.contextParseTree;
Tree aHead = aTree.getLeaves().get(candidate.headIndex).ancestor(1, aTree);
for (Tree node : aTree.pathNodeToNode(aHead, aTree)) {
sb.append(node.value()).append("-");
if (node.value().equals("S"))
break;
}
features.incrementCount("B-A-SYNPATH-" + sb.toString());
}
features.incrementCount("A-FIRSTAPPEAR", aC.representative.sentNum);
features.incrementCount("M-FIRSTAPPEAR", mC.representative.sentNum);
// document size in # of sentences
int docSize = document.predictedMentions.size();
features.incrementCount("A-FIRSTAPPEAR-NORMALIZED", aC.representative.sentNum / docSize);
features.incrementCount("M-FIRSTAPPEAR-NORMALIZED", mC.representative.sentNum / docSize);
}
////////////////////////////////////////////////////////////////////////////////
if (HybridCorefProperties.useMentionDetectionFeatures(props, sievename)) {
// bare plurals
if (m.originalSpan.size() == 1 && m.headWord.tag().equals("NNS"))
features.incrementCount("B-M-BAREPLURAL");
if (candidate.originalSpan.size() == 1 && candidate.headWord.tag().equals("NNS"))
features.incrementCount("B-A-BAREPLURAL");
// pleonastic it
if (CorefProperties.useConstituencyParse(props)) {
if (RuleBasedCorefMentionFinder.isPleonastic(m, m.contextParseTree) || RuleBasedCorefMentionFinder.isPleonastic(candidate, candidate.contextParseTree)) {
features.incrementCount("B-PLEONASTICIT");
}
}
// quantRule
if (dict.quantifiers.contains(mFirst.word().toLowerCase(Locale.ENGLISH)))
features.incrementCount("B-M-QUANTIFIER");
if (dict.quantifiers.contains(aFirst.word().toLowerCase(Locale.ENGLISH)))
features.incrementCount("B-A-QUANTIFIER");
// starts with negation
if (mFirst.word().toLowerCase(Locale.ENGLISH).matches("none|no|nothing|not") || aFirst.word().toLowerCase(Locale.ENGLISH).matches("none|no|nothing|not")) {
features.incrementCount("B-NEGATIVE-START");
}
// parititive rule
if (RuleBasedCorefMentionFinder.partitiveRule(m, m.sentenceWords, dict))
features.incrementCount("B-M-PARTITIVE");
if (RuleBasedCorefMentionFinder.partitiveRule(candidate, candidate.sentenceWords, dict))
features.incrementCount("B-A-PARTITIVE");
// %
if (m.headString.equals("%"))
features.incrementCount("B-M-HEAD%");
if (candidate.headString.equals("%"))
features.incrementCount("B-A-HEAD%");
// adjective form of nations
if (dict.isAdjectivalDemonym(m.spanToString()))
features.incrementCount("B-M-ADJ-DEMONYM");
if (dict.isAdjectivalDemonym(candidate.spanToString()))
features.incrementCount("B-A-ADJ-DEMONYM");
// ends with "etc."
if (m.lowercaseNormalizedSpanString().endsWith("etc."))
features.incrementCount("B-M-ETC-END");
if (candidate.lowercaseNormalizedSpanString().endsWith("etc."))
features.incrementCount("B-A-ETC-END");
}
////////////////////////////////////////////////////////////////////////////////
/////// attributes, attributes agree ////////////
////////////////////////////////////////////////////////////////////////////////
features.incrementCount("B-M-NUMBER-" + m.number);
features.incrementCount("B-A-NUMBER-" + candidate.number);
features.incrementCount("B-M-GENDER-" + m.gender);
features.incrementCount("B-A-GENDER-" + candidate.gender);
features.incrementCount("B-M-ANIMACY-" + m.animacy);
features.incrementCount("B-A-ANIMACY-" + candidate.animacy);
features.incrementCount("B-M-PERSON-" + m.person);
features.incrementCount("B-A-PERSON-" + candidate.person);
features.incrementCount("B-M-NETYPE-" + m.nerString);
features.incrementCount("B-A-NETYPE-" + candidate.nerString);
features.incrementCount("B-BOTH-NUMBER-" + candidate.number + "-" + m.number);
features.incrementCount("B-BOTH-GENDER-" + candidate.gender + "-" + m.gender);
features.incrementCount("B-BOTH-ANIMACY-" + candidate.animacy + "-" + m.animacy);
features.incrementCount("B-BOTH-PERSON-" + candidate.person + "-" + m.person);
features.incrementCount("B-BOTH-NETYPE-" + candidate.nerString + "-" + m.nerString);
Set<Number> mcNumber = Generics.newHashSet();
for (Number n : mC.numbers) {
features.incrementCount("B-MC-NUMBER-" + n);
mcNumber.add(n);
}
if (mcNumber.size() == 1) {
features.incrementCount("B-MC-CLUSTERNUMBER-" + mcNumber.iterator().next());
} else {
mcNumber.remove(Number.UNKNOWN);
if (mcNumber.size() == 1)
features.incrementCount("B-MC-CLUSTERNUMBER-" + mcNumber.iterator().next());
else
features.incrementCount("B-MC-CLUSTERNUMBER-CONFLICT");
}
Set<Gender> mcGender = Generics.newHashSet();
for (Gender g : mC.genders) {
features.incrementCount("B-MC-GENDER-" + g);
mcGender.add(g);
}
if (mcGender.size() == 1) {
features.incrementCount("B-MC-CLUSTERGENDER-" + mcGender.iterator().next());
} else {
mcGender.remove(Gender.UNKNOWN);
if (mcGender.size() == 1)
features.incrementCount("B-MC-CLUSTERGENDER-" + mcGender.iterator().next());
else
features.incrementCount("B-MC-CLUSTERGENDER-CONFLICT");
}
Set<Animacy> mcAnimacy = Generics.newHashSet();
for (Animacy a : mC.animacies) {
features.incrementCount("B-MC-ANIMACY-" + a);
mcAnimacy.add(a);
}
if (mcAnimacy.size() == 1) {
features.incrementCount("B-MC-CLUSTERANIMACY-" + mcAnimacy.iterator().next());
} else {
mcAnimacy.remove(Animacy.UNKNOWN);
if (mcAnimacy.size() == 1)
features.incrementCount("B-MC-CLUSTERANIMACY-" + mcAnimacy.iterator().next());
else
features.incrementCount("B-MC-CLUSTERANIMACY-CONFLICT");
}
Set<String> mcNER = Generics.newHashSet();
for (String t : mC.nerStrings) {
features.incrementCount("B-MC-NETYPE-" + t);
mcNER.add(t);
}
if (mcNER.size() == 1) {
features.incrementCount("B-MC-CLUSTERNETYPE-" + mcNER.iterator().next());
} else {
mcNER.remove("O");
if (mcNER.size() == 1)
features.incrementCount("B-MC-CLUSTERNETYPE-" + mcNER.iterator().next());
else
features.incrementCount("B-MC-CLUSTERNETYPE-CONFLICT");
}
Set<Number> acNumber = Generics.newHashSet();
for (Number n : aC.numbers) {
features.incrementCount("B-AC-NUMBER-" + n);
acNumber.add(n);
}
if (acNumber.size() == 1) {
features.incrementCount("B-AC-CLUSTERNUMBER-" + acNumber.iterator().next());
} else {
acNumber.remove(Number.UNKNOWN);
if (acNumber.size() == 1)
features.incrementCount("B-AC-CLUSTERNUMBER-" + acNumber.iterator().next());
else
features.incrementCount("B-AC-CLUSTERNUMBER-CONFLICT");
}
Set<Gender> acGender = Generics.newHashSet();
for (Gender g : aC.genders) {
features.incrementCount("B-AC-GENDER-" + g);
acGender.add(g);
}
if (acGender.size() == 1) {
features.incrementCount("B-AC-CLUSTERGENDER-" + acGender.iterator().next());
} else {
acGender.remove(Gender.UNKNOWN);
if (acGender.size() == 1)
features.incrementCount("B-AC-CLUSTERGENDER-" + acGender.iterator().next());
else
features.incrementCount("B-AC-CLUSTERGENDER-CONFLICT");
}
Set<Animacy> acAnimacy = Generics.newHashSet();
for (Animacy a : aC.animacies) {
features.incrementCount("B-AC-ANIMACY-" + a);
acAnimacy.add(a);
}
if (acAnimacy.size() == 1) {
features.incrementCount("B-AC-CLUSTERANIMACY-" + acAnimacy.iterator().next());
} else {
acAnimacy.remove(Animacy.UNKNOWN);
if (acAnimacy.size() == 1)
features.incrementCount("B-AC-CLUSTERANIMACY-" + acAnimacy.iterator().next());
else
features.incrementCount("B-AC-CLUSTERANIMACY-CONFLICT");
}
Set<String> acNER = Generics.newHashSet();
for (String t : aC.nerStrings) {
features.incrementCount("B-AC-NETYPE-" + t);
acNER.add(t);
}
if (acNER.size() == 1) {
features.incrementCount("B-AC-CLUSTERNETYPE-" + acNER.iterator().next());
} else {
acNER.remove("O");
if (acNER.size() == 1)
features.incrementCount("B-AC-CLUSTERNETYPE-" + acNER.iterator().next());
else
features.incrementCount("B-AC-CLUSTERNETYPE-CONFLICT");
}
if (m.numbersAgree(candidate))
features.incrementCount("B-NUMBER-AGREE");
if (m.gendersAgree(candidate))
features.incrementCount("B-GENDER-AGREE");
if (m.animaciesAgree(candidate))
features.incrementCount("B-ANIMACY-AGREE");
if (CorefRules.entityAttributesAgree(mC, aC))
features.incrementCount("B-ATTRIBUTES-AGREE");
if (CorefRules.entityPersonDisagree(document, m, candidate, dict))
features.incrementCount("B-PERSON-DISAGREE");
////////////////////////////////////////////////////////////////////////////////
if (HybridCorefProperties.useDcorefRules(props, sievename)) {
if (CorefRules.entityIWithinI(m, candidate, dict))
features.incrementCount("B-i-within-i");
if (CorefRules.antecedentIsMentionSpeaker(document, m, candidate, dict))
features.incrementCount("B-ANT-IS-SPEAKER");
if (CorefRules.entitySameSpeaker(document, m, candidate))
features.incrementCount("B-SAME-SPEAKER");
if (CorefRules.entitySubjectObject(m, candidate))
features.incrementCount("B-SUBJ-OBJ");
for (Mention a : aC.corefMentions) {
if (CorefRules.entitySubjectObject(m, a))
features.incrementCount("B-CLUSTER-SUBJ-OBJ");
}
if (CorefRules.entityPersonDisagree(document, m, candidate, dict) && CorefRules.entitySameSpeaker(document, m, candidate))
features.incrementCount("B-PERSON-DISAGREE-SAME-SPEAKER");
if (CorefRules.entityIWithinI(mC, aC, dict))
features.incrementCount("B-ENTITY-IWITHINI");
if (CorefRules.antecedentMatchesMentionSpeakerAnnotation(m, candidate, document))
features.incrementCount("B-ANT-IS-SPEAKER-OF-MENTION");
Set<MentionType> mType = HybridCorefProperties.getMentionType(props, sievename);
if (mType.contains(MentionType.PROPER) || mType.contains(MentionType.NOMINAL)) {
if (m.headString.equals(candidate.headString))
features.incrementCount("B-HEADMATCH");
if (CorefRules.entityHeadsAgree(mC, aC, m, candidate, dict))
features.incrementCount("B-HEADSAGREE");
if (CorefRules.entityExactStringMatch(mC, aC, dict, document.roleSet))
features.incrementCount("B-EXACTSTRINGMATCH");
if (CorefRules.entityHaveExtraProperNoun(m, candidate, new HashSet<>()))
features.incrementCount("B-HAVE-EXTRA-PROPER-NOUN");
if (CorefRules.entityBothHaveProper(mC, aC))
features.incrementCount("B-BOTH-HAVE-PROPER");
if (CorefRules.entityHaveDifferentLocation(m, candidate, dict))
features.incrementCount("B-HAVE-DIFF-LOC");
if (CorefRules.entityHaveIncompatibleModifier(mC, aC))
features.incrementCount("B-HAVE-INCOMPATIBLE-MODIFIER");
if (CorefRules.entityIsAcronym(document, mC, aC))
features.incrementCount("B-IS-ACRONYM");
if (CorefRules.entityIsApposition(mC, aC, m, candidate))
features.incrementCount("B-IS-APPOSITION");
if (CorefRules.entityIsPredicateNominatives(mC, aC, m, candidate))
features.incrementCount("B-IS-PREDICATE-NOMINATIVES");
if (CorefRules.entityIsRoleAppositive(mC, aC, m, candidate, dict))
features.incrementCount("B-IS-ROLE-APPOSITIVE");
if (CorefRules.entityNumberInLaterMention(m, candidate))
features.incrementCount("B-NUMBER-IN-LATER");
if (CorefRules.entityRelaxedExactStringMatch(mC, aC, m, candidate, dict, document.roleSet))
features.incrementCount("B-RELAXED-EXACT-STRING-MATCH");
if (CorefRules.entityRelaxedHeadsAgreeBetweenMentions(mC, aC, m, candidate))
features.incrementCount("B-RELAXED-HEAD-AGREE");
if (CorefRules.entitySameProperHeadLastWord(m, candidate))
features.incrementCount("B-SAME-PROPER-HEAD");
if (CorefRules.entitySameProperHeadLastWord(mC, aC, m, candidate))
features.incrementCount("B-CLUSTER-SAME-PROPER-HEAD");
if (CorefRules.entityWordsIncluded(mC, aC, m, candidate))
features.incrementCount("B-WORD-INCLUSION");
}
if (mType.contains(MentionType.LIST)) {
features.incrementCount("NUM-LIST-", numEntitiesInList(m));
if (m.spanToString().contains("two") || m.spanToString().contains("2") || m.spanToString().contains("both"))
features.incrementCount("LIST-M-TWO");
if (m.spanToString().contains("three") || m.spanToString().contains("3"))
features.incrementCount("LIST-M-THREE");
if (candidate.spanToString().contains("two") || candidate.spanToString().contains("2") || candidate.spanToString().contains("both")) {
features.incrementCount("B-LIST-A-TWO");
}
if (candidate.spanToString().contains("three") || candidate.spanToString().contains("3")) {
features.incrementCount("B-LIST-A-THREE");
}
}
if (mType.contains(MentionType.PRONOMINAL)) {
if (dict.firstPersonPronouns.contains(m.headString))
features.incrementCount("B-M-I");
if (dict.secondPersonPronouns.contains(m.headString))
features.incrementCount("B-M-YOU");
if (dict.thirdPersonPronouns.contains(m.headString))
features.incrementCount("B-M-3RDPERSON");
if (dict.possessivePronouns.contains(m.headString))
features.incrementCount("B-M-POSSESSIVE");
if (dict.neutralPronouns.contains(m.headString))
features.incrementCount("B-M-NEUTRAL");
if (dict.malePronouns.contains(m.headString))
features.incrementCount("B-M-MALE");
if (dict.femalePronouns.contains(m.headString))
features.incrementCount("B-M-FEMALE");
if (dict.firstPersonPronouns.contains(candidate.headString))
features.incrementCount("B-A-I");
if (dict.secondPersonPronouns.contains(candidate.headString))
features.incrementCount("B-A-YOU");
if (dict.thirdPersonPronouns.contains(candidate.headString))
features.incrementCount("B-A-3RDPERSON");
if (dict.possessivePronouns.contains(candidate.headString))
features.incrementCount("B-A-POSSESSIVE");
if (dict.neutralPronouns.contains(candidate.headString))
features.incrementCount("B-A-NEUTRAL");
if (dict.malePronouns.contains(candidate.headString))
features.incrementCount("B-A-MALE");
if (dict.femalePronouns.contains(candidate.headString))
features.incrementCount("B-A-FEMALE");
features.incrementCount("B-M-GENERIC-" + m.generic);
features.incrementCount("B-A-GENERIC-" + candidate.generic);
if (HybridCorefPrinter.dcorefPronounSieve.skipThisMention(document, m, mC, dict)) {
features.incrementCount("B-SKIPTHISMENTION-true");
}
if (m.spanToString().equalsIgnoreCase("you") && mFollowing != null && mFollowing.word().equalsIgnoreCase("know")) {
features.incrementCount("B-YOUKNOW-PRECEDING-POS-" + ((mPreceding == null) ? "NULL" : mPreceding.tag()));
features.incrementCount("B-YOUKNOW-PRECEDING-WORD-" + ((mPreceding == null) ? "NULL" : mPreceding.word().toLowerCase()));
CoreLabel nextword = (m.endIndex + 1 < m.sentenceWords.size()) ? m.sentenceWords.get(m.endIndex + 1) : null;
features.incrementCount("B-YOUKNOW-FOLLOWING-POS-" + ((nextword == null) ? "NULL" : nextword.tag()));
features.incrementCount("B-YOUKNOW-FOLLOWING-WORD-" + ((nextword == null) ? "NULL" : nextword.word().toLowerCase()));
}
if (candidate.spanToString().equalsIgnoreCase("you") && aFollowing != null && aFollowing.word().equalsIgnoreCase("know")) {
features.incrementCount("B-YOUKNOW-PRECEDING-POS-" + ((aPreceding == null) ? "NULL" : aPreceding.tag()));
features.incrementCount("B-YOUKNOW-PRECEDING-WORD-" + ((aPreceding == null) ? "NULL" : aPreceding.word().toLowerCase()));
CoreLabel nextword = (candidate.endIndex + 1 < candidate.sentenceWords.size()) ? candidate.sentenceWords.get(candidate.endIndex + 1) : null;
features.incrementCount("B-YOUKNOW-FOLLOWING-POS-" + ((nextword == null) ? "NULL" : nextword.tag()));
features.incrementCount("B-YOUKNOW-FOLLOWING-WORD-" + ((nextword == null) ? "NULL" : nextword.word().toLowerCase()));
}
}
// discourse match features
if (m.person == Person.YOU && document.docType == DocType.ARTICLE && m.headWord.get(CoreAnnotations.SpeakerAnnotation.class).equals("PER0")) {
features.incrementCount("B-DISCOURSE-M-YOU-GENERIC?");
}
if (candidate.generic && candidate.person == Person.YOU)
features.incrementCount("B-DISCOURSE-A-YOU-GENERIC?");
String mString = m.lowercaseNormalizedSpanString();
String antString = candidate.lowercaseNormalizedSpanString();
// I-I
if (m.number == Number.SINGULAR && dict.firstPersonPronouns.contains(mString) && candidate.number == Number.SINGULAR && dict.firstPersonPronouns.contains(antString) && CorefRules.entitySameSpeaker(document, m, candidate)) {
features.incrementCount("B-DISCOURSE-I-I-SAMESPEAKER");
}
// (speaker - I)
if ((m.number == Number.SINGULAR && dict.firstPersonPronouns.contains(mString)) && CorefRules.antecedentIsMentionSpeaker(document, m, candidate, dict)) {
features.incrementCount("B-DISCOURSE-SPEAKER-I");
}
// (I - speaker)
if ((candidate.number == Number.SINGULAR && dict.firstPersonPronouns.contains(antString)) && CorefRules.antecedentIsMentionSpeaker(document, candidate, m, dict)) {
features.incrementCount("B-DISCOURSE-I-SPEAKER");
}
// Can be iffy if more than two speakers... but still should be okay most of the time
if (dict.secondPersonPronouns.contains(mString) && dict.secondPersonPronouns.contains(antString) && CorefRules.entitySameSpeaker(document, m, candidate)) {
features.incrementCount("B-DISCOURSE-BOTH-YOU");
}
// previous I - you or previous you - I in two person conversation
if (((m.person == Person.I && candidate.person == Person.YOU || (m.person == Person.YOU && candidate.person == Person.I)) && (m.headWord.get(CoreAnnotations.UtteranceAnnotation.class) - candidate.headWord.get(CoreAnnotations.UtteranceAnnotation.class) == 1) && document.docType == DocType.CONVERSATION)) {
features.incrementCount("B-DISCOURSE-I-YOU");
}
if (dict.reflexivePronouns.contains(m.headString) && CorefRules.entitySubjectObject(m, candidate)) {
features.incrementCount("B-DISCOURSE-REFLEXIVE");
}
if (m.person == Person.I && candidate.person == Person.I && !CorefRules.entitySameSpeaker(document, m, candidate)) {
features.incrementCount("B-DISCOURSE-I-I-DIFFSPEAKER");
}
if (m.person == Person.YOU && candidate.person == Person.YOU && !CorefRules.entitySameSpeaker(document, m, candidate)) {
features.incrementCount("B-DISCOURSE-YOU-YOU-DIFFSPEAKER");
}
if (m.person == Person.WE && candidate.person == Person.WE && !CorefRules.entitySameSpeaker(document, m, candidate)) {
features.incrementCount("B-DISCOURSE-WE-WE-DIFFSPEAKER");
}
}
////////////////////////////////////////////////////////////////////////////////
if (HybridCorefProperties.usePOSFeatures(props, sievename)) {
features.incrementCount("B-LEXICAL-M-HEADPOS-" + m.headWord.tag());
features.incrementCount("B-LEXICAL-A-HEADPOS-" + candidate.headWord.tag());
features.incrementCount("B-LEXICAL-M-FIRSTPOS-" + mFirst.tag());
features.incrementCount("B-LEXICAL-A-FIRSTPOS-" + aFirst.tag());
features.incrementCount("B-LEXICAL-M-LASTPOS-" + mLast.tag());
features.incrementCount("B-LEXICAL-A-LASTPOS-" + aLast.tag());
features.incrementCount("B-LEXICAL-M-PRECEDINGPOS-" + ((mPreceding == null) ? "NULL" : mPreceding.tag()));
features.incrementCount("B-LEXICAL-A-PRECEDINGPOS-" + ((aPreceding == null) ? "NULL" : aPreceding.tag()));
features.incrementCount("B-LEXICAL-M-FOLLOWINGPOS-" + ((mFollowing == null) ? "NULL" : mFollowing.tag()));
features.incrementCount("B-LEXICAL-A-FOLLOWINGPOS-" + ((aFollowing == null) ? "NULL" : aFollowing.tag()));
}
////////////////////////////////////////////////////////////////////////////////
if (HybridCorefProperties.useLexicalFeatures(props, sievename)) {
features.incrementCount("B-LEXICAL-M-HEADWORD-" + m.headString.toLowerCase());
features.incrementCount("B-LEXICAL-A-HEADWORD-" + candidate.headString.toLowerCase());
features.incrementCount("B-LEXICAL-M-FIRSTWORD-" + mFirst.word().toLowerCase());
features.incrementCount("B-LEXICAL-A-FIRSTWORD-" + aFirst.word().toLowerCase());
features.incrementCount("B-LEXICAL-M-LASTWORD-" + mLast.word().toLowerCase());
features.incrementCount("B-LEXICAL-A-LASTWORD-" + aLast.word().toLowerCase());
features.incrementCount("B-LEXICAL-M-PRECEDINGWORD-" + ((mPreceding == null) ? "NULL" : mPreceding.word().toLowerCase()));
features.incrementCount("B-LEXICAL-A-PRECEDINGWORD-" + ((aPreceding == null) ? "NULL" : aPreceding.word().toLowerCase()));
features.incrementCount("B-LEXICAL-M-FOLLOWINGWORD-" + ((mFollowing == null) ? "NULL" : mFollowing.word().toLowerCase()));
features.incrementCount("B-LEXICAL-A-FOLLOWINGWORD-" + ((aFollowing == null) ? "NULL" : aFollowing.word().toLowerCase()));
//extra headword, modifiers lexical features
for (String mHead : mC.heads) {
if (!aC.heads.contains(mHead))
features.incrementCount("B-LEXICAL-MC-EXTRAHEAD-" + mHead);
}
for (String mWord : mC.words) {
if (!aC.words.contains(mWord))
features.incrementCount("B-LEXICAL-MC-EXTRAWORD-" + mWord);
}
}
// cosine
if (HybridCorefProperties.useWordEmbedding(props, sievename)) {
// dimension
int dim = dict.vectors.entrySet().iterator().next().getValue().length;
// distance between headword
float[] mV = dict.vectors.get(m.headString.toLowerCase());
float[] aV = dict.vectors.get(candidate.headString.toLowerCase());
if (mV != null && aV != null) {
features.incrementCount("WORDVECTOR-DIFF-HEADWORD", cosine(mV, aV));
}
mV = dict.vectors.get(mFirst.word().toLowerCase());
aV = dict.vectors.get(aFirst.word().toLowerCase());
if (mV != null && aV != null) {
features.incrementCount("WORDVECTOR-DIFF-FIRSTWORD", cosine(mV, aV));
}
mV = dict.vectors.get(mLast.word().toLowerCase());
aV = dict.vectors.get(aLast.word().toLowerCase());
if (mV != null && aV != null) {
features.incrementCount("WORDVECTOR-DIFF-LASTWORD", cosine(mV, aV));
}
if (mPreceding != null && aPreceding != null) {
mV = dict.vectors.get(mPreceding.word().toLowerCase());
aV = dict.vectors.get(aPreceding.word().toLowerCase());
if (mV != null && aV != null) {
features.incrementCount("WORDVECTOR-DIFF-PRECEDINGWORD", cosine(mV, aV));
}
}
if (mFollowing != null && aFollowing != null) {
mV = dict.vectors.get(mFollowing.word().toLowerCase());
aV = dict.vectors.get(aFollowing.word().toLowerCase());
if (mV != null && aV != null) {
features.incrementCount("WORDVECTOR-DIFF-FOLLOWINGWORD", cosine(mV, aV));
}
}
float[] aggreM = new float[dim];
float[] aggreA = new float[dim];
for (CoreLabel cl : m.originalSpan) {
float[] v = dict.vectors.get(cl.word().toLowerCase());
if (v == null)
continue;
ArrayMath.pairwiseAddInPlace(aggreM, v);
}
for (CoreLabel cl : candidate.originalSpan) {
float[] v = dict.vectors.get(cl.word().toLowerCase());
if (v == null)
continue;
ArrayMath.pairwiseAddInPlace(aggreA, v);
}
if (ArrayMath.L2Norm(aggreM) != 0 && ArrayMath.L2Norm(aggreA) != 0) {
features.incrementCount("WORDVECTOR-AGGREGATE-DIFF", cosine(aggreM, aggreA));
}
int cnt = 0;
double dist = 0;
for (CoreLabel mcl : m.originalSpan) {
for (CoreLabel acl : candidate.originalSpan) {
mV = dict.vectors.get(mcl.word().toLowerCase());
aV = dict.vectors.get(acl.word().toLowerCase());
if (mV == null || aV == null)
continue;
cnt++;
dist += cosine(mV, aV);
}
}
features.incrementCount("WORDVECTOR-AVG-DIFF", dist / cnt);
}
return new RVFDatum<>(features, label);
} catch (Exception e) {
log.info("Datum Extraction failed in Sieve.java while processing document: " + document.docInfo.get("DOC_ID") + " part: " + document.docInfo.get("DOC_PART"));
throw new RuntimeException(e);
}
}
use of edu.stanford.nlp.ling.CoreLabel in project CoreNLP by stanfordnlp.
the class CorefMentionFinder method removeSpuriousMentionsEn.
protected void removeSpuriousMentionsEn(Annotation doc, List<List<Mention>> predictedMentions, Dictionaries dict) {
List<CoreMap> sentences = doc.get(CoreAnnotations.SentencesAnnotation.class);
for (int i = 0; i < predictedMentions.size(); i++) {
CoreMap s = sentences.get(i);
List<Mention> mentions = predictedMentions.get(i);
List<CoreLabel> sent = s.get(CoreAnnotations.TokensAnnotation.class);
Set<Mention> remove = Generics.newHashSet();
for (Mention m : mentions) {
String headPOS = m.headWord.get(CoreAnnotations.PartOfSpeechAnnotation.class);
// non word such as 'hmm'
if (dict.nonWords.contains(m.headString))
remove.add(m);
// check if the mention is noun and the next word is not noun
if (dict.isAdjectivalDemonym(m.spanToString())) {
if (!headPOS.startsWith("N") || (m.endIndex < sent.size() && sent.get(m.endIndex).tag().startsWith("N"))) {
remove.add(m);
}
}
// stop list (e.g., U.S., there)
if (inStopList(m))
remove.add(m);
}
mentions.removeAll(remove);
}
}
use of edu.stanford.nlp.ling.CoreLabel in project CoreNLP by stanfordnlp.
the class CorefMentionFinder method findSyntacticHead.
public Tree findSyntacticHead(Mention m, Tree root, List<CoreLabel> tokens) {
// mention ends with 's
int endIdx = m.endIndex;
if (m.originalSpan.size() > 0) {
String lastWord = m.originalSpan.get(m.originalSpan.size() - 1).get(CoreAnnotations.TextAnnotation.class);
if ((lastWord.equals("'s") || lastWord.equals("'")) && m.originalSpan.size() != 1)
endIdx--;
}
Tree exactMatch = findTreeWithSpan(root, m.startIndex, endIdx);
//
if (exactMatch != null) {
return safeHead(exactMatch, endIdx);
}
// context, so as to make the parser work better :-)
if (allowReparsing) {
int approximateness = 0;
List<CoreLabel> extentTokens = new ArrayList<>();
extentTokens.add(initCoreLabel("It"));
extentTokens.add(initCoreLabel("was"));
final int ADDED_WORDS = 2;
for (int i = m.startIndex; i < endIdx; i++) {
// Add everything except separated dashes! The separated dashes mess with the parser too badly.
CoreLabel label = tokens.get(i);
if (!"-".equals(label.word())) {
extentTokens.add(tokens.get(i));
} else {
approximateness++;
}
}
extentTokens.add(initCoreLabel("."));
// constrain the parse to the part we're interested in.
// Starting from ADDED_WORDS comes from skipping "It was".
// -1 to exclude the period.
// We now let it be any kind of nominal constituent, since there
// are VP and S ones
ParserConstraint constraint = new ParserConstraint(ADDED_WORDS, extentTokens.size() - 1, Pattern.compile(".*"));
List<ParserConstraint> constraints = Collections.singletonList(constraint);
Tree tree = parse(extentTokens, constraints);
// now unnecessary, as parser uses CoreLabels?
convertToCoreLabels(tree);
// remember it has ADDED_WORDS extra words at the beginning
tree.indexSpans(m.startIndex - ADDED_WORDS);
Tree subtree = findPartialSpan(tree, m.startIndex);
// There was a possible problem that with a crazy parse, extentHead could be one of the added words, not a real word!
// Now we make sure in findPartialSpan that it can't be before the real start, and in safeHead, we disallow something
// passed the right end (that is, just that final period).
Tree extentHead = safeHead(subtree, endIdx);
assert (extentHead != null);
// extentHead is a child in the local extent parse tree. we need to find the corresponding node in the main tree
// Because we deleted dashes, it's index will be >= the index in the extent parse tree
CoreLabel l = (CoreLabel) extentHead.label();
Tree realHead = funkyFindLeafWithApproximateSpan(root, l.value(), l.get(CoreAnnotations.BeginIndexAnnotation.class), approximateness);
assert (realHead != null);
return realHead;
}
// If reparsing wasn't allowed, try to find a span in the tree
// which happens to have the head
Tree wordMatch = findTreeWithSmallestSpan(root, m.startIndex, endIdx);
if (wordMatch != null) {
Tree head = safeHead(wordMatch, endIdx);
if (head != null) {
int index = ((CoreLabel) head.label()).get(CoreAnnotations.IndexAnnotation.class) - 1;
if (index >= m.startIndex && index < endIdx) {
return head;
}
}
}
// If that didn't work, guess that it's the last word
int lastNounIdx = endIdx - 1;
for (int i = m.startIndex; i < m.endIndex; i++) {
if (tokens.get(i).tag().startsWith("N"))
lastNounIdx = i;
else if (tokens.get(i).tag().startsWith("W"))
break;
}
List<Tree> leaves = root.getLeaves();
Tree endLeaf = leaves.get(lastNounIdx);
return endLeaf;
}
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