Examples with ClauseClassifierLabel edu.stanford.nlp.naturalli.ClauseSplitter.ClauseClassifierLabel used on opensource projects

Example 1 with ClauseClassifierLabel

use of edu.stanford.nlp.naturalli.ClauseSplitter.ClauseClassifierLabel in project CoreNLP by stanfordnlp.

the class ClauseSplitterSearchProblem method search.

/**
   * The core implementation of the search.
   *
   * @param root The root word to search from. Traditionally, this is the root of the sentence.
   * @param candidateFragments The callback for the resulting sentence fragments.
   *                           This is a predicate of a triple of values.
   *                           The return value of the predicate determines whether we should continue searching.
   *                           The triple is a triple of
   *                           <ol>
   *                             <li>The log probability of the sentence fragment, according to the featurizer and the weights</li>
   *                             <li>The features along the path to this fragment. The last element of this is the features from the most recent step.</li>
   *                             <li>The sentence fragment. Because it is relatively expensive to compute the resulting tree, this is returned as a lazy {@link Supplier}.</li>
   *                           </ol>
   * @param classifier The classifier for whether an arc should be on the path to a clause split, a clause split itself, or neither.
   * @param featurizer The featurizer to use. Make sure this matches the weights!
   * @param actionSpace The action space we are allowed to take. Each action defines a means of splitting a clause on a dependency boundary.
   */
protected void search(// The root to search from
IndexedWord root, // The output specs
final Predicate<Triple<Double, List<Counter<String>>, Supplier<SentenceFragment>>> candidateFragments, // The learning specs
final Classifier<ClauseSplitter.ClauseClassifierLabel, String> classifier, Map<String, ? extends List<String>> hardCodedSplits, final Function<Triple<State, Action, State>, Counter<String>> featurizer, final Collection<Action> actionSpace, final int maxTicks) {
    // (the fringe)
    PriorityQueue<Pair<State, List<Counter<String>>>> fringe = new FixedPrioritiesPriorityQueue<>();
    // (avoid duplicate work)
    Set<IndexedWord> seenWords = new HashSet<>();
    State firstState = new State(null, null, -9000, null, x -> {
    }, // First state is implicitly "done"
    true);
    fringe.add(Pair.makePair(firstState, new ArrayList<>(0)), -0.0);
    int ticks = 0;
    while (!fringe.isEmpty()) {
        if (++ticks > maxTicks) {
            //        log.info("WARNING! Timed out on search with " + ticks + " ticks");
            return;
        }
        // Useful variables
        double logProbSoFar = fringe.getPriority();
        assert logProbSoFar <= 0.0;
        Pair<State, List<Counter<String>>> lastStatePair = fringe.removeFirst();
        State lastState = lastStatePair.first;
        List<Counter<String>> featuresSoFar = lastStatePair.second;
        IndexedWord rootWord = lastState.edge == null ? root : lastState.edge.getDependent();
        // Register thunk
        if (lastState.isDone) {
            if (!candidateFragments.test(Triple.makeTriple(logProbSoFar, featuresSoFar, () -> {
                SemanticGraph copy = new SemanticGraph(tree);
                lastState.thunk.andThen(x -> {
                    for (IndexedWord newTreeRoot : x.getRoots()) {
                        if (newTreeRoot != null) {
                            for (SemanticGraphEdge extraEdge : extraEdgesByGovernor.get(newTreeRoot)) {
                                assert Util.isTree(x);
                                addSubtree(x, newTreeRoot, extraEdge.getRelation().toString(), tree, extraEdge.getDependent(), tree.getIncomingEdgesSorted(newTreeRoot));
                                assert Util.isTree(x);
                            }
                        }
                    }
                }).accept(copy);
                return new SentenceFragment(copy, assumedTruth, false);
            }))) {
                break;
            }
        }
        // Find relevant auxilliary terms
        SemanticGraphEdge subjOrNull = null;
        SemanticGraphEdge objOrNull = null;
        for (SemanticGraphEdge auxEdge : tree.outgoingEdgeIterable(rootWord)) {
            String relString = auxEdge.getRelation().toString();
            if (relString.contains("obj")) {
                objOrNull = auxEdge;
            } else if (relString.contains("subj")) {
                subjOrNull = auxEdge;
            }
        }
        // For each outgoing edge...
        for (SemanticGraphEdge outgoingEdge : tree.outgoingEdgeIterable(rootWord)) {
            // This fires if the governor is an indirect speech verb, and the outgoing edge is a ccomp
            if (outgoingEdge.getRelation().toString().equals("ccomp") && ((outgoingEdge.getGovernor().lemma() != null && INDIRECT_SPEECH_LEMMAS.contains(outgoingEdge.getGovernor().lemma())) || INDIRECT_SPEECH_LEMMAS.contains(outgoingEdge.getGovernor().word()))) {
                continue;
            }
            // Get some variables
            String outgoingEdgeRelation = outgoingEdge.getRelation().toString();
            List<String> forcedArcOrder = hardCodedSplits.get(outgoingEdgeRelation);
            if (forcedArcOrder == null && outgoingEdgeRelation.contains(":")) {
                forcedArcOrder = hardCodedSplits.get(outgoingEdgeRelation.substring(0, outgoingEdgeRelation.indexOf(":")) + ":*");
            }
            boolean doneForcedArc = false;
            // For each action...
            for (Action action : (forcedArcOrder == null ? actionSpace : orderActions(actionSpace, forcedArcOrder))) {
                // Check the prerequisite
                if (!action.prerequisitesMet(tree, outgoingEdge)) {
                    continue;
                }
                if (forcedArcOrder != null && doneForcedArc) {
                    break;
                }
                // 1. Compute the child state
                Optional<State> candidate = action.applyTo(tree, lastState, outgoingEdge, subjOrNull, objOrNull);
                if (candidate.isPresent()) {
                    double logProbability;
                    ClauseClassifierLabel bestLabel;
                    Counter<String> features = featurizer.apply(Triple.makeTriple(lastState, action, candidate.get()));
                    if (forcedArcOrder != null && !doneForcedArc) {
                        logProbability = 0.0;
                        bestLabel = ClauseClassifierLabel.CLAUSE_SPLIT;
                        doneForcedArc = true;
                    } else if (features.containsKey("__undocumented_junit_no_classifier")) {
                        logProbability = Double.NEGATIVE_INFINITY;
                        bestLabel = ClauseClassifierLabel.CLAUSE_INTERM;
                    } else {
                        Counter<ClauseClassifierLabel> scores = classifier.scoresOf(new RVFDatum<>(features));
                        if (scores.size() > 0) {
                            Counters.logNormalizeInPlace(scores);
                        }
                        String rel = outgoingEdge.getRelation().toString();
                        if ("nsubj".equals(rel) || "dobj".equals(rel)) {
                            // Always at least yield on nsubj and dobj
                            scores.remove(ClauseClassifierLabel.NOT_A_CLAUSE);
                        }
                        logProbability = Counters.max(scores, Double.NEGATIVE_INFINITY);
                        bestLabel = Counters.argmax(scores, (x, y) -> 0, ClauseClassifierLabel.CLAUSE_SPLIT);
                    }
                    if (bestLabel != ClauseClassifierLabel.NOT_A_CLAUSE) {
                        Pair<State, List<Counter<String>>> childState = Pair.makePair(candidate.get().withIsDone(bestLabel), new ArrayList<Counter<String>>(featuresSoFar) {

                            {
                                add(features);
                            }
                        });
                        // 2. Register the child state
                        if (!seenWords.contains(childState.first.edge.getDependent())) {
                            //            log.info("  pushing " + action.signature() + " with " + argmax.first.edge);
                            fringe.add(childState, logProbability);
                        }
                    }
                }
            }
        }
        seenWords.add(rootWord);
    }
//    log.info("Search finished in " + ticks + " ticks and " + classifierEvals + " classifier evaluations.");
}