Examples with Mention edu.stanford.nlp.coref.data.Mention used on opensource projects

Example 6 with Mention

use of edu.stanford.nlp.coref.data.Mention 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);
    }
}

Example 7 with Mention

use of edu.stanford.nlp.coref.data.Mention in project CoreNLP by stanfordnlp.

the class RFSieve method findCoreferentAntecedent.

public void findCoreferentAntecedent(Mention m, int mIdx, Document document, Dictionaries dict, Properties props, StringBuilder sbLog) throws Exception {
    int sentIdx = m.sentNum;
    Counter<Integer> probs = new ClassicCounter<>();
    int mentionDist = 0;
    for (int sentDist = 0; sentDist <= Math.min(this.maxSentDist, sentIdx); sentDist++) {
        List<Mention> candidates = getOrderedAntecedents(m, sentIdx - sentDist, mIdx, document.predictedMentions, dict);
        for (Mention candidate : candidates) {
            if (skipForAnalysis(candidate, m, props))
                continue;
            if (candidate == m)
                continue;
            if (!aType.contains(candidate.mentionType))
                continue;
            if (m.mentionType == MentionType.PRONOMINAL) {
                if (!matchedMentionType(m, mTypeStr))
                    continue;
                if (!matchedMentionType(candidate, aTypeStr))
                    continue;
            }
            // ignore cataphora
            if (sentDist == 0 && m.appearEarlierThan(candidate))
                continue;
            mentionDist++;
            RVFDatum<Boolean, String> datum = extractDatum(m, candidate, document, mentionDist, dict, props, sievename);
            double probTrue = 0;
            if (this.classifierType == ClassifierType.RF) {
                probTrue = this.rf.probabilityOfTrue(datum);
            }
            probs.setCount(candidate.mentionID, probTrue);
        }
    }
    if (HybridCorefProperties.debug(props)) {
        sbLog.append(HybridCorefPrinter.printErrorLog(m, document, probs, mIdx, dict, this));
    }
    if (probs.size() > 0 && Counters.max(probs) > this.thresMerge) {
        // merge highest prob candidate
        int antID = Counters.argmax(probs);
        Sieve.merge(document, m.mentionID, antID);
    }
}

Example 8 with Mention

use of edu.stanford.nlp.coref.data.Mention in project CoreNLP by stanfordnlp.

the class Sieve method sortMentionsByClause.

/** Divides a sentence into clauses and sort the antecedents for pronoun matching  */
private static List<Mention> sortMentionsByClause(List<Mention> l, Mention m1) {
    List<Mention> sorted = new ArrayList<>();
    Tree tree = m1.contextParseTree;
    Tree current = m1.mentionSubTree;
    if (tree == null || current == null)
        return l;
    while (true) {
        current = current.ancestor(1, tree);
        String curLabel = current.label().value();
        if ("TOP".equals(curLabel) || curLabel.startsWith("S") || curLabel.equals("NP")) {
            //      if(current.label().value().startsWith("S")){
            for (Mention m : l) {
                if (!sorted.contains(m) && current.dominates(m.mentionSubTree))
                    sorted.add(m);
            }
        }
        if (current.ancestor(1, tree) == null)
            break;
    }
    return sorted;
}

Example 9 with Mention

use of edu.stanford.nlp.coref.data.Mention 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);
    }
}

Example 10 with Mention

use of edu.stanford.nlp.coref.data.Mention in project CoreNLP by stanfordnlp.

the class CorefMentionFinder method addGoldMentions.

// temporary for debug
protected static void addGoldMentions(List<CoreMap> sentences, List<Set<IntPair>> mentionSpanSetList, List<List<Mention>> predictedMentions, List<List<Mention>> allGoldMentions) {
    for (int i = 0, sz = sentences.size(); i < sz; i++) {
        List<Mention> mentions = predictedMentions.get(i);
        CoreMap sent = sentences.get(i);
        List<CoreLabel> tokens = sent.get(TokensAnnotation.class);
        Set<IntPair> mentionSpanSet = mentionSpanSetList.get(i);
        List<Mention> golds = allGoldMentions.get(i);
        for (Mention g : golds) {
            IntPair pair = new IntPair(g.startIndex, g.endIndex);
            if (!mentionSpanSet.contains(pair)) {
                int dummyMentionId = -1;
                Mention m = new Mention(dummyMentionId, g.startIndex, g.endIndex, tokens, sent.get(SemanticGraphCoreAnnotations.BasicDependenciesAnnotation.class), sent.get(SemanticGraphCoreAnnotations.EnhancedDependenciesAnnotation.class) != null ? sent.get(SemanticGraphCoreAnnotations.EnhancedDependenciesAnnotation.class) : sent.get(SemanticGraphCoreAnnotations.BasicDependenciesAnnotation.class), new ArrayList<>(tokens.subList(g.startIndex, g.endIndex)));
                mentions.add(m);
                mentionSpanSet.add(pair);
            }
        }
    }
}