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Example 11 with SemanticGraphEdge

use of edu.stanford.nlp.semgraph.SemanticGraphEdge in project CoreNLP by stanfordnlp.

the class RelationTripleSegmenter method extract.

/**
   * Extract the nominal patterns from this sentence.
   *
   * @see RelationTripleSegmenter#NOUN_TOKEN_PATTERNS
   * @see RelationTripleSegmenter#NOUN_DEPENDENCY_PATTERNS
   *
   * @param parse The parse tree of the sentence to annotate.
   * @param tokens The tokens of the sentence to annotate.
   * @return A list of {@link RelationTriple}s. Note that these do not have an associated tree with them.
   */
@SuppressWarnings("unchecked")
public List<RelationTriple> extract(SemanticGraph parse, List<CoreLabel> tokens) {
    List<RelationTriple> extractions = new ArrayList<>();
    Set<Triple<Span, String, Span>> alreadyExtracted = new HashSet<>();
    //
    for (TokenSequencePattern tokenPattern : NOUN_TOKEN_PATTERNS) {
        TokenSequenceMatcher tokenMatcher = tokenPattern.matcher(tokens);
        while (tokenMatcher.find()) {
            boolean missingPrefixBe;
            boolean missingSuffixOf = false;
            // Create subject
            List<? extends CoreMap> subject = tokenMatcher.groupNodes("$subject");
            Span subjectSpan = Util.extractNER(tokens, Span.fromValues(((CoreLabel) subject.get(0)).index() - 1, ((CoreLabel) subject.get(subject.size() - 1)).index()));
            List<CoreLabel> subjectTokens = new ArrayList<>();
            for (int i : subjectSpan) {
                subjectTokens.add(tokens.get(i));
            }
            // Create object
            List<? extends CoreMap> object = tokenMatcher.groupNodes("$object");
            Span objectSpan = Util.extractNER(tokens, Span.fromValues(((CoreLabel) object.get(0)).index() - 1, ((CoreLabel) object.get(object.size() - 1)).index()));
            if (Span.overlaps(subjectSpan, objectSpan)) {
                continue;
            }
            List<CoreLabel> objectTokens = new ArrayList<>();
            for (int i : objectSpan) {
                objectTokens.add(tokens.get(i));
            }
            // Create relation
            if (subjectTokens.size() > 0 && objectTokens.size() > 0) {
                List<CoreLabel> relationTokens = new ArrayList<>();
                // (add the 'be')
                missingPrefixBe = true;
                // (add a complement to the 'be')
                List<? extends CoreMap> beofComp = tokenMatcher.groupNodes("$beof_comp");
                if (beofComp != null) {
                    // (add the complement
                    for (CoreMap token : beofComp) {
                        if (token instanceof CoreLabel) {
                            relationTokens.add((CoreLabel) token);
                        } else {
                            relationTokens.add(new CoreLabel(token));
                        }
                    }
                    // (add the 'of')
                    missingSuffixOf = true;
                }
                // Add extraction
                String relationGloss = StringUtils.join(relationTokens.stream().map(CoreLabel::word), " ");
                if (!alreadyExtracted.contains(Triple.makeTriple(subjectSpan, relationGloss, objectSpan))) {
                    RelationTriple extraction = new RelationTriple(subjectTokens, relationTokens, objectTokens);
                    //noinspection ConstantConditions
                    extraction.isPrefixBe(missingPrefixBe);
                    extraction.isSuffixOf(missingSuffixOf);
                    extractions.add(extraction);
                    alreadyExtracted.add(Triple.makeTriple(subjectSpan, relationGloss, objectSpan));
                }
            }
        }
        //
        for (SemgrexPattern semgrex : NOUN_DEPENDENCY_PATTERNS) {
            SemgrexMatcher matcher = semgrex.matcher(parse);
            while (matcher.find()) {
                boolean missingPrefixBe = false;
                boolean missingSuffixBe = false;
                boolean istmod = false;
                // Get relaux if applicable
                String relaux = matcher.getRelnString("relaux");
                String ignoredArc = relaux;
                if (ignoredArc == null) {
                    ignoredArc = matcher.getRelnString("arc");
                }
                // Create subject
                IndexedWord subject = matcher.getNode("subject");
                List<IndexedWord> subjectTokens = new ArrayList<>();
                Span subjectSpan;
                if (subject.ner() != null && !"O".equals(subject.ner())) {
                    subjectSpan = Util.extractNER(tokens, Span.fromValues(subject.index() - 1, subject.index()));
                    for (int i : subjectSpan) {
                        subjectTokens.add(new IndexedWord(tokens.get(i)));
                    }
                } else {
                    subjectTokens = getValidChunk(parse, subject, VALID_SUBJECT_ARCS, Optional.ofNullable(ignoredArc), true).orElse(Collections.singletonList(subject));
                    subjectSpan = Util.tokensToSpan(subjectTokens);
                }
                // Create object
                IndexedWord object = matcher.getNode("object");
                List<IndexedWord> objectTokens = new ArrayList<>();
                Span objectSpan;
                if (object.ner() != null && !"O".equals(object.ner())) {
                    objectSpan = Util.extractNER(tokens, Span.fromValues(object.index() - 1, object.index()));
                    for (int i : objectSpan) {
                        objectTokens.add(new IndexedWord(tokens.get(i)));
                    }
                } else {
                    objectTokens = getValidChunk(parse, object, VALID_OBJECT_ARCS, Optional.ofNullable(ignoredArc), true).orElse(Collections.singletonList(object));
                    objectSpan = Util.tokensToSpan(objectTokens);
                }
                // Check that the pair is valid
                if (Span.overlaps(subjectSpan, objectSpan)) {
                    // We extracted an identity
                    continue;
                }
                if (subjectSpan.end() == objectSpan.start() - 1 && (tokens.get(subjectSpan.end()).word().matches("[\\.,:;\\('\"]") || "CC".equals(tokens.get(subjectSpan.end()).tag()))) {
                    // We're straddling a clause
                    continue;
                }
                if (objectSpan.end() == subjectSpan.start() - 1 && (tokens.get(objectSpan.end()).word().matches("[\\.,:;\\('\"]") || "CC".equals(tokens.get(objectSpan.end()).tag()))) {
                    // We're straddling a clause
                    continue;
                }
                // Get any prepositional edges
                String expected = relaux == null ? "" : relaux.substring(relaux.indexOf(":") + 1).replace("_", " ");
                IndexedWord prepWord = null;
                // (these usually come from the object)
                boolean prepositionIsPrefix = false;
                for (SemanticGraphEdge edge : parse.outgoingEdgeIterable(object)) {
                    if (edge.getRelation().toString().equals("case")) {
                        prepWord = edge.getDependent();
                    }
                }
                // (...but sometimes from the subject)
                if (prepWord == null) {
                    for (SemanticGraphEdge edge : parse.outgoingEdgeIterable(subject)) {
                        if (edge.getRelation().toString().equals("case")) {
                            prepositionIsPrefix = true;
                            prepWord = edge.getDependent();
                        }
                    }
                }
                List<IndexedWord> prepChunk = Collections.EMPTY_LIST;
                if (prepWord != null && !expected.equals("tmod")) {
                    Optional<List<IndexedWord>> optionalPrepChunk = getValidChunk(parse, prepWord, Collections.singleton("mwe"), Optional.empty(), true);
                    if (!optionalPrepChunk.isPresent()) {
                        continue;
                    }
                    prepChunk = optionalPrepChunk.get();
                    Collections.sort(prepChunk, (a, b) -> {
                        double val = a.pseudoPosition() - b.pseudoPosition();
                        if (val < 0) {
                            return -1;
                        }
                        if (val > 0) {
                            return 1;
                        } else {
                            return 0;
                        }
                    });
                // ascending sort
                }
                // Get the relation
                if (subjectTokens.size() > 0 && objectTokens.size() > 0) {
                    LinkedList<IndexedWord> relationTokens = new LinkedList<>();
                    IndexedWord relNode = matcher.getNode("relation");
                    if (relNode != null) {
                        // Case: we have a grounded relation span
                        // (add the relation)
                        relationTokens.add(relNode);
                        // (add any prepositional case markings)
                        if (prepositionIsPrefix) {
                            // We're almost certainly missing a suffix 'be'
                            missingSuffixBe = true;
                            for (int i = prepChunk.size() - 1; i >= 0; --i) {
                                relationTokens.addFirst(prepChunk.get(i));
                            }
                        } else {
                            relationTokens.addAll(prepChunk);
                        }
                        if (expected.equalsIgnoreCase("tmod")) {
                            istmod = true;
                        }
                    } else {
                        // (mark it as missing a preceding 'be'
                        if (!expected.equals("poss")) {
                            missingPrefixBe = true;
                        }
                        // (add any prepositional case markings)
                        if (prepositionIsPrefix) {
                            for (int i = prepChunk.size() - 1; i >= 0; --i) {
                                relationTokens.addFirst(prepChunk.get(i));
                            }
                        } else {
                            relationTokens.addAll(prepChunk);
                        }
                        if (expected.equalsIgnoreCase("tmod")) {
                            istmod = true;
                        }
                        // (some fine-tuning)
                        if (allowNominalsWithoutNER && "of".equals(expected)) {
                            // prohibit things like "conductor of electricity" -> "conductor; be of; electricity"
                            continue;
                        }
                    }
                    // Add extraction
                    String relationGloss = StringUtils.join(relationTokens.stream().map(IndexedWord::word), " ");
                    if (!alreadyExtracted.contains(Triple.makeTriple(subjectSpan, relationGloss, objectSpan))) {
                        RelationTriple extraction = new RelationTriple(subjectTokens.stream().map(IndexedWord::backingLabel).collect(Collectors.toList()), relationTokens.stream().map(IndexedWord::backingLabel).collect(Collectors.toList()), objectTokens.stream().map(IndexedWord::backingLabel).collect(Collectors.toList()));
                        extraction.istmod(istmod);
                        extraction.isPrefixBe(missingPrefixBe);
                        extraction.isSuffixBe(missingSuffixBe);
                        extractions.add(extraction);
                        alreadyExtracted.add(Triple.makeTriple(subjectSpan, relationGloss, objectSpan));
                    }
                }
            }
        }
    }
    //
    // Filter downward polarity extractions
    //
    Iterator<RelationTriple> iter = extractions.iterator();
    while (iter.hasNext()) {
        RelationTriple term = iter.next();
        boolean shouldRemove = true;
        for (CoreLabel token : term) {
            if (token.get(NaturalLogicAnnotations.PolarityAnnotation.class) == null || !token.get(NaturalLogicAnnotations.PolarityAnnotation.class).isDownwards()) {
                shouldRemove = false;
            }
        }
        if (shouldRemove) {
            // Don't extract things in downward polarity contexts.
            iter.remove();
        }
    }
    // Return
    return extractions;
}
Also used : SemgrexMatcher(edu.stanford.nlp.semgraph.semgrex.SemgrexMatcher) Span(edu.stanford.nlp.ie.machinereading.structure.Span) TokenSequencePattern(edu.stanford.nlp.ling.tokensregex.TokenSequencePattern) RelationTriple(edu.stanford.nlp.ie.util.RelationTriple) SemgrexPattern(edu.stanford.nlp.semgraph.semgrex.SemgrexPattern) TokenSequenceMatcher(edu.stanford.nlp.ling.tokensregex.TokenSequenceMatcher) SemanticGraphEdge(edu.stanford.nlp.semgraph.SemanticGraphEdge) RelationTriple(edu.stanford.nlp.ie.util.RelationTriple) CoreLabel(edu.stanford.nlp.ling.CoreLabel) IndexedWord(edu.stanford.nlp.ling.IndexedWord)

Example 12 with SemanticGraphEdge

use of edu.stanford.nlp.semgraph.SemanticGraphEdge in project CoreNLP by stanfordnlp.

the class ClauseSplitterSearchProblem method addSubtree.

/**
   * A helper to add an entire subtree to a given dependency tree.
   *
   * @param toModify The tree to add the subtree to.
   * @param root The root of the tree where we should be adding the subtree.
   * @param rel The relation to add the subtree with.
   * @param originalTree The orignal tree (i.e., {@link ClauseSplitterSearchProblem#tree}).
   * @param subject The root of the clause to add.
   * @param ignoredEdges The edges to ignore adding when adding this subtree.
   */
private static void addSubtree(SemanticGraph toModify, IndexedWord root, String rel, SemanticGraph originalTree, IndexedWord subject, Collection<SemanticGraphEdge> ignoredEdges) {
    if (toModify.containsVertex(subject)) {
        // This subtree already exists.
        return;
    }
    Queue<IndexedWord> fringe = new LinkedList<>();
    Collection<IndexedWord> wordsToAdd = new ArrayList<>();
    Collection<SemanticGraphEdge> edgesToAdd = new ArrayList<>();
    // Search for subtree to add
    for (SemanticGraphEdge edge : originalTree.outgoingEdgeIterable(subject)) {
        if (!ignoredEdges.contains(edge)) {
            if (toModify.containsVertex(edge.getDependent())) {
                // Case: we're adding a subtree that's not disjoint from toModify. This is bad news.
                return;
            }
            edgesToAdd.add(edge);
            fringe.add(edge.getDependent());
        }
    }
    while (!fringe.isEmpty()) {
        IndexedWord node = fringe.poll();
        wordsToAdd.add(node);
        for (SemanticGraphEdge edge : originalTree.outgoingEdgeIterable(node)) {
            if (!ignoredEdges.contains(edge)) {
                if (toModify.containsVertex(edge.getDependent())) {
                    // Case: we're adding a subtree that's not disjoint from toModify. This is bad news.
                    return;
                }
                edgesToAdd.add(edge);
                fringe.add(edge.getDependent());
            }
        }
    }
    // Add subtree
    // (add subject)
    toModify.addVertex(subject);
    toModify.addEdge(root, subject, GrammaticalRelation.valueOf(Language.English, rel), Double.NEGATIVE_INFINITY, false);
    // (add nodes)
    wordsToAdd.forEach(toModify::addVertex);
    // (add edges)
    for (SemanticGraphEdge edge : edgesToAdd) {
        assert !toModify.incomingEdgeIterator(edge.getDependent()).hasNext();
        toModify.addEdge(edge.getGovernor(), edge.getDependent(), edge.getRelation(), edge.getWeight(), edge.isExtra());
    }
}
Also used : SemanticGraphEdge(edu.stanford.nlp.semgraph.SemanticGraphEdge)

Example 13 with SemanticGraphEdge

use of edu.stanford.nlp.semgraph.SemanticGraphEdge in project CoreNLP by stanfordnlp.

the class NaturalLogicAnnotator method annotateOperators.

/**
   * Find the operators in this sentence, annotating the head word (only!) of each operator with the
   * {@link edu.stanford.nlp.naturalli.NaturalLogicAnnotations.OperatorAnnotation}.
   *
   * @param sentence As in {@link edu.stanford.nlp.naturalli.NaturalLogicAnnotator#doOneSentence(edu.stanford.nlp.pipeline.Annotation, edu.stanford.nlp.util.CoreMap)}
   */
private void annotateOperators(CoreMap sentence) {
    SemanticGraph tree = sentence.get(SemanticGraphCoreAnnotations.BasicDependenciesAnnotation.class);
    List<CoreLabel> tokens = sentence.get(CoreAnnotations.TokensAnnotation.class);
    if (tree == null) {
        tree = sentence.get(SemanticGraphCoreAnnotations.EnhancedDependenciesAnnotation.class);
    }
    for (SemgrexPattern pattern : PATTERNS) {
        SemgrexMatcher matcher = pattern.matcher(tree);
        while (matcher.find()) {
            // Get terms
            IndexedWord properSubject = matcher.getNode("Subject");
            IndexedWord quantifier, subject;
            boolean namedEntityQuantifier = false;
            if (properSubject != null) {
                quantifier = subject = properSubject;
                namedEntityQuantifier = true;
            } else {
                quantifier = matcher.getNode("quantifier");
                subject = matcher.getNode("subject");
            }
            // Validate quantifier
            // At the end of this
            Optional<Triple<Operator, Integer, Integer>> quantifierInfo;
            if (namedEntityQuantifier) {
                // named entities have the "all" semantics by default.
                if (!neQuantifiers) {
                    continue;
                }
                // note: empty quantifier span given
                quantifierInfo = Optional.of(Triple.makeTriple(Operator.IMPLICIT_NAMED_ENTITY, quantifier.index(), quantifier.index()));
            } else {
                // find the quantifier, and return some info about it.
                quantifierInfo = validateQuantifierByHead(sentence, quantifier);
            }
            // (fix up 'there are')
            if ("be".equals(subject == null ? null : subject.lemma())) {
                boolean hasExpl = false;
                IndexedWord newSubject = null;
                for (SemanticGraphEdge outgoingEdge : tree.outgoingEdgeIterable(subject)) {
                    if ("nsubj".equals(outgoingEdge.getRelation().toString())) {
                        newSubject = outgoingEdge.getDependent();
                    } else if ("expl".equals(outgoingEdge.getRelation().toString())) {
                        hasExpl = true;
                    }
                }
                if (hasExpl) {
                    subject = newSubject;
                }
            }
            // (fix up '$n$ of')
            if ("CD".equals(subject == null ? null : subject.tag())) {
                for (SemanticGraphEdge outgoingEdge : tree.outgoingEdgeIterable(subject)) {
                    String rel = outgoingEdge.getRelation().toString();
                    if (rel.startsWith("nmod")) {
                        subject = outgoingEdge.getDependent();
                    }
                }
            }
            // Set tokens
            if (quantifierInfo.isPresent()) {
                // Compute span
                OperatorSpec scope = computeScope(tree, quantifierInfo.get().first, matcher.getNode("pivot"), Pair.makePair(quantifierInfo.get().second, quantifierInfo.get().third), subject, namedEntityQuantifier, matcher.getNode("object"), tokens.size());
                // Set annotation
                CoreLabel token = sentence.get(CoreAnnotations.TokensAnnotation.class).get(quantifier.index() - 1);
                OperatorSpec oldScope = token.get(OperatorAnnotation.class);
                if (oldScope == null || oldScope.quantifierLength() < scope.quantifierLength() || oldScope.instance != scope.instance) {
                    token.set(OperatorAnnotation.class, scope);
                } else {
                    token.set(OperatorAnnotation.class, OperatorSpec.merge(oldScope, scope));
                }
            }
        }
    }
    // Ensure we didn't select overlapping quantifiers. For example, "a" and "a few" can often overlap.
    // In these cases, take the longer quantifier match.
    List<OperatorSpec> quantifiers = new ArrayList<>();
    sentence.get(CoreAnnotations.TokensAnnotation.class).stream().filter(token -> token.containsKey(OperatorAnnotation.class)).forEach(token -> quantifiers.add(token.get(OperatorAnnotation.class)));
    quantifiers.sort((x, y) -> y.quantifierLength() - x.quantifierLength());
    for (OperatorSpec quantifier : quantifiers) {
        for (int i = quantifier.quantifierBegin; i < quantifier.quantifierEnd; ++i) {
            if (i != quantifier.quantifierHead) {
                tokens.get(i).remove(OperatorAnnotation.class);
            }
        }
    }
}
Also used : SemgrexMatcher(edu.stanford.nlp.semgraph.semgrex.SemgrexMatcher) CoreLabel(edu.stanford.nlp.ling.CoreLabel) java.util(java.util) CoreAnnotations(edu.stanford.nlp.ling.CoreAnnotations) SemanticGraphEdge(edu.stanford.nlp.semgraph.SemanticGraphEdge) Redwood(edu.stanford.nlp.util.logging.Redwood) edu.stanford.nlp.util(edu.stanford.nlp.util) SentenceAnnotator(edu.stanford.nlp.pipeline.SentenceAnnotator) SemgrexMatcher(edu.stanford.nlp.semgraph.semgrex.SemgrexMatcher) NaturalLogicAnnotations(edu.stanford.nlp.naturalli.NaturalLogicAnnotations) Function(java.util.function.Function) Collectors(java.util.stream.Collectors) Span(edu.stanford.nlp.ie.machinereading.structure.Span) CoreAnnotation(edu.stanford.nlp.ling.CoreAnnotation) Annotation(edu.stanford.nlp.pipeline.Annotation) SemanticGraphCoreAnnotations(edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations) TokenSequenceMatcher(edu.stanford.nlp.ling.tokensregex.TokenSequenceMatcher) SemanticGraph(edu.stanford.nlp.semgraph.SemanticGraph) SemgrexPattern(edu.stanford.nlp.semgraph.semgrex.SemgrexPattern) IndexedWord(edu.stanford.nlp.ling.IndexedWord) TokenSequencePattern(edu.stanford.nlp.ling.tokensregex.TokenSequencePattern) SemgrexPattern(edu.stanford.nlp.semgraph.semgrex.SemgrexPattern) SemanticGraphCoreAnnotations(edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations) SemanticGraphEdge(edu.stanford.nlp.semgraph.SemanticGraphEdge) CoreLabel(edu.stanford.nlp.ling.CoreLabel) CoreAnnotations(edu.stanford.nlp.ling.CoreAnnotations) SemanticGraphCoreAnnotations(edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations) SemanticGraph(edu.stanford.nlp.semgraph.SemanticGraph) IndexedWord(edu.stanford.nlp.ling.IndexedWord)

Example 14 with SemanticGraphEdge

use of edu.stanford.nlp.semgraph.SemanticGraphEdge in project CoreNLP by stanfordnlp.

the class NaturalLogicWeights method objDeletionProbability.

public double objDeletionProbability(SemanticGraphEdge edge, Iterable<SemanticGraphEdge> neighbors) {
    // Get information about the neighbors
    // (in a totally not-creepy-stalker sort of way)
    Optional<String> subj = Optional.empty();
    Optional<String> pp = Optional.empty();
    for (SemanticGraphEdge neighbor : neighbors) {
        if (neighbor != edge) {
            String neighborRel = neighbor.getRelation().toString();
            if (neighborRel.contains("subj")) {
                subj = Optional.of(neighbor.getDependent().originalText().toLowerCase());
            }
            if (neighborRel.contains("prep")) {
                pp = Optional.of(neighborRel);
            }
            if (neighborRel.contains("obj")) {
                // allow deleting second object
                return 1.0;
            }
        }
    }
    String obj = edge.getDependent().originalText().toLowerCase();
    String verb = edge.getGovernor().originalText().toLowerCase();
    // Compute the most informative drop probability we can
    Double rawScore = null;
    if (subj.isPresent()) {
        if (pp.isPresent()) {
            // Case: subj+obj
            rawScore = verbSubjPPObjAffinity.get(Quadruple.makeQuadruple(verb, subj.get(), pp.get(), obj));
        }
    }
    if (rawScore == null) {
        rawScore = verbObjAffinity.get(verb);
    }
    if (rawScore == null) {
        return deletionProbability(edge.getRelation().toString());
    } else {
        return 1.0 - Math.min(1.0, rawScore / upperProbabilityCap);
    }
}
Also used : SemanticGraphEdge(edu.stanford.nlp.semgraph.SemanticGraphEdge)

Example 15 with SemanticGraphEdge

use of edu.stanford.nlp.semgraph.SemanticGraphEdge in project CoreNLP by stanfordnlp.

the class RelationTripleSegmenter method segment.

/**
   * <p>
   * Try to segment this sentence as a relation triple.
   * This sentence must already match one of a few strict patterns for a valid OpenIE extraction.
   * If it does not, then no relation triple is created.
   * That is, this is <b>not</b> a relation extractor; it is just a utility to segment what is already a
   * (subject, relation, object) triple into these three parts.
   * </p>
   *
   * <p>
   *   This method will attempt to use both the verb-centric patterns and the ACL-centric patterns.
   * </p>
   *
   * @param parse The sentence to process, as a dependency tree.
   * @param confidence An optional confidence to pass on to the relation triple.
   * @param consumeAll if true, force the entire parse to be consumed by the pattern.
   * @return A relation triple, if this sentence matches one of the patterns of a valid relation triple.
   */
public Optional<RelationTriple> segment(SemanticGraph parse, Optional<Double> confidence, boolean consumeAll) {
    // Copy and clean the tree
    parse = new SemanticGraph(parse);
    // Special case "there is <something>". Arguably this is a job for the clause splitter, but the <something> is
    // sometimes not _really_ its own clause
    IndexedWord root = parse.getFirstRoot();
    if ((root.lemma() != null && root.lemma().equalsIgnoreCase("be")) || (root.lemma() == null && ("is".equalsIgnoreCase(root.word()) || "are".equalsIgnoreCase(root.word()) || "were".equalsIgnoreCase(root.word()) || "be".equalsIgnoreCase(root.word())))) {
        // Check for the "there is" construction
        boolean foundThere = false;
        // an indicator for there being too much nonsense hanging off of the root
        boolean tooMayArcs = false;
        Optional<SemanticGraphEdge> newRoot = Optional.empty();
        for (SemanticGraphEdge edge : parse.outgoingEdgeIterable(root)) {
            if (edge.getRelation().toString().equals("expl") && edge.getDependent().word().equalsIgnoreCase("there")) {
                foundThere = true;
            } else if (edge.getRelation().toString().equals("nsubj")) {
                newRoot = Optional.of(edge);
            } else {
                tooMayArcs = true;
            }
        }
        // Split off "there is")
        if (foundThere && newRoot.isPresent() && !tooMayArcs) {
            ClauseSplitterSearchProblem.splitToChildOfEdge(parse, newRoot.get());
        }
    }
    // Run the patterns
    Optional<RelationTriple> extraction = segmentVerb(parse, confidence, consumeAll);
    if (!extraction.isPresent()) {
        extraction = segmentACL(parse, confidence, consumeAll);
    }
    //
    if (extraction.isPresent()) {
        boolean shouldRemove = true;
        for (CoreLabel token : extraction.get()) {
            if (token.get(NaturalLogicAnnotations.PolarityAnnotation.class) == null || !token.get(NaturalLogicAnnotations.PolarityAnnotation.class).isDownwards()) {
                shouldRemove = false;
            }
        }
        if (shouldRemove) {
            return Optional.empty();
        }
    }
    // Return
    return extraction;
}
Also used : CoreLabel(edu.stanford.nlp.ling.CoreLabel) RelationTriple(edu.stanford.nlp.ie.util.RelationTriple) SemanticGraph(edu.stanford.nlp.semgraph.SemanticGraph) IndexedWord(edu.stanford.nlp.ling.IndexedWord) SemanticGraphEdge(edu.stanford.nlp.semgraph.SemanticGraphEdge)

Aggregations

SemanticGraphEdge (edu.stanford.nlp.semgraph.SemanticGraphEdge)65 IndexedWord (edu.stanford.nlp.ling.IndexedWord)52 SemanticGraph (edu.stanford.nlp.semgraph.SemanticGraph)21 CoreLabel (edu.stanford.nlp.ling.CoreLabel)15 GrammaticalRelation (edu.stanford.nlp.trees.GrammaticalRelation)15 CoreAnnotations (edu.stanford.nlp.ling.CoreAnnotations)11 SemgrexMatcher (edu.stanford.nlp.semgraph.semgrex.SemgrexMatcher)10 SemanticGraphCoreAnnotations (edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations)8 Pair (edu.stanford.nlp.util.Pair)6 Mention (edu.stanford.nlp.coref.data.Mention)5 Span (edu.stanford.nlp.ie.machinereading.structure.Span)5 Annotation (edu.stanford.nlp.pipeline.Annotation)5 Tree (edu.stanford.nlp.trees.Tree)5 CoreMap (edu.stanford.nlp.util.CoreMap)5 HashMap (java.util.HashMap)5 Collectors (java.util.stream.Collectors)5 RelationTriple (edu.stanford.nlp.ie.util.RelationTriple)4 SemgrexPattern (edu.stanford.nlp.semgraph.semgrex.SemgrexPattern)4 IntPair (edu.stanford.nlp.util.IntPair)4 java.util (java.util)4