Esempio n. 1
0
 // Output in .rc form
 public String toString() {
   StringBuilder rc = new StringBuilder();
   for (String key : keySet()) {
     List<Triple> list = getTriples(key);
     for (Triple triple : list) {
       String line = triple.toString();
       rc.append(line);
       rc.append("\n");
     }
   }
   return rc.toString();
 }
Esempio n. 2
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  /**
   * Populates the namespaces map with default namespaces and namespaces used by the graph.
   *
   * @param graph Graph
   * @throws GraphException
   */
  protected void populateNamespaces(Graph graph) throws GraphException {

    // default namespaces
    namespaces = new HashMap<String, String>();
    namespaces.put(RDF_PREFIX, RDF.BASE_URI.toString());
    namespaces.put(RDFS_PREFIX, RDFS.BASE_URI.toString());
    namespaces.put("owl", "http://www.w3.org/2002/07/owl#");
    namespaces.put("dc", "http://purl.org/dc/elements/1.1/");

    // validate graph before reading
    if (graph == null) {
      throw new IllegalArgumentException("Graph argument is null.");
    }

    // get all statements
    ClosableIterator<Triple> tripleIter = graph.find(null, null, null);

    if (tripleIter != null) {

      while (tripleIter.hasNext()) {

        // get the next triple
        Triple triple = tripleIter.next();

        if (triple != null) {

          // evaluate subject
          SubjectNode subject = triple.getSubject();
          if (subject instanceof URIReference) {
            addNamespaceURI(((URIReference) subject).getURI());
          }

          // evaluate predicate (must be URIReference)
          PredicateNode predicate = triple.getPredicate();
          addNamespaceURI(((URIReference) predicate).getURI());

          // evaluate object
          ObjectNode object = triple.getObject();
          if (object instanceof URIReference) {
            addNamespaceURI(((URIReference) object).getURI());
          }
        }
      }

      // close the Iterator
      tripleIter.close();
    }
  }
Esempio n. 3
0
  private void addColliders(
      Graph graph, final SepsetProducer sepsetProducer, IKnowledge knowledge) {
    final Map<Triple, Double> collidersPs =
        findCollidersUsingSepsets(sepsetProducer, graph, verbose, knowledge);

    List<Triple> colliders = new ArrayList<>(collidersPs.keySet());

    Collections.shuffle(colliders);

    Collections.sort(
        colliders,
        new Comparator<Triple>() {
          public int compare(Triple o1, Triple o2) {
            return -Double.compare(collidersPs.get(o1), collidersPs.get(o2));
          }
        });

    if (trueDag != null) {
      for (Triple collider : colliders) {
        Node a = collider.getX();
        Node b = collider.getY();
        Node c = collider.getZ();

        List<Node> sep = trueDag.getSepset(a, c);

        System.out.println(
            "JJJ "
                + collider
                + " collider = "
                + (sep != null && !sep.contains(b))
                + " p = "
                + collidersPs.get(collider));
      }
    }

    for (Triple collider : colliders) {
      Node a = collider.getX();
      Node b = collider.getY();
      Node c = collider.getZ();

      if (!(isArrowpointAllowed(a, b, knowledge) && isArrowpointAllowed(c, b, knowledge))) {
        continue;
      }

      if (!graph.getEdge(a, b).pointsTowards(a) && !graph.getEdge(b, c).pointsTowards(c)) {
        graph.setEndpoint(a, b, Endpoint.ARROW);
        graph.setEndpoint(c, b, Endpoint.ARROW);
      }
    }
  }
  /**
   * 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>The features along the path to this fragment. The last element of this is the
   *           features from the most recent step.
   *       <li>The sentence fragment. Because it is relatively expensive to compute the resulting
   *           tree, this is returned as a lazy {@link Supplier}.
   *     </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 -> {}, true); // First state is implicitly "done"
    fringe.add(Pair.makePair(firstState, new ArrayList<>(0)), -0.0);
    int ticks = 0;

    while (!fringe.isEmpty()) {
      if (++ticks > maxTicks) {
        //        System.err.println("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 -> {
                            // Add the extra edges back in, if they don't break the tree-ness of the
                            // extraction
                            for (IndexedWord newTreeRoot : x.getRoots()) {
                              if (newTreeRoot != null) { // what a strange thing to have happen...
                                for (SemanticGraphEdge extraEdge :
                                    extraEdgesByGovernor.get(newTreeRoot)) {
                                  assert Util.isTree(x);
                                  //noinspection unchecked
                                  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;
        }
      }

      // Iterate over children
      // For each outgoing edge...
      for (SemanticGraphEdge outgoingEdge : tree.outgoingEdgeIterable(rootWord)) {
        // Prohibit indirect speech verbs from splitting off clauses
        // (e.g., 'said', 'think')
        // 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)) {
                scores.remove(
                    ClauseClassifierLabel.NOT_A_CLAUSE); // Always at least yield on nsubj and dobj
              }
              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())) {
                //            System.err.println("  pushing " + action.signature() + " with " +
                // argmax.first.edge);
                fringe.add(childState, logProbability);
              }
            }
          }
        }
      }

      seenWords.add(rootWord);
    }
    //    System.err.println("Search finished in " + ticks + " ticks and " + classifierEvals + "
    // classifier evaluations.");
  }