Exemple #1
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  /**
   * Evaluates how many words (= terminals) in a collection of trees are covered by the lexicon.
   * First arg is the collection of trees; second through fourth args get the results. Currently
   * unused; this probably only works if train and test at same time so tags and words variables are
   * initialized.
   */
  public double evaluateCoverage(
      Collection<Tree> trees,
      Set<String> missingWords,
      Set<String> missingTags,
      Set<IntTaggedWord> missingTW) {

    List<IntTaggedWord> iTW1 = new ArrayList<IntTaggedWord>();
    for (Tree t : trees) {
      iTW1.addAll(treeToEvents(t));
    }

    int total = 0;
    int unseen = 0;

    for (IntTaggedWord itw : iTW1) {
      total++;
      if (!words.contains(new IntTaggedWord(itw.word(), nullTag))) {
        missingWords.add(wordIndex.get(itw.word()));
      }
      if (!tags.contains(new IntTaggedWord(nullWord, itw.tag()))) {
        missingTags.add(tagIndex.get(itw.tag()));
      }
      // if (!rules.contains(itw)) {
      if (seenCounter.getCount(itw) == 0.0) {
        unseen++;
        missingTW.add(itw);
      }
    }
    return (double) unseen / total;
  }
Exemple #2
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  /**
   * Writes out data from this Object to the Writer w. Rules are separated by newline, and rule
   * elements are delimited by \t.
   */
  public void writeData(Writer w) throws IOException {
    PrintWriter out = new PrintWriter(w);

    for (IntTaggedWord itw : seenCounter.keySet()) {
      out.println(itw.toLexicalEntry(wordIndex, tagIndex) + " SEEN " + seenCounter.getCount(itw));
    }
    for (IntTaggedWord itw : getUnknownWordModel().unSeenCounter().keySet()) {
      out.println(
          itw.toLexicalEntry(wordIndex, tagIndex)
              + " UNSEEN "
              + getUnknownWordModel().unSeenCounter().getCount(itw));
    }
    for (int i = 0; i < smooth.length; i++) {
      out.println("smooth[" + i + "] = " + smooth[i]);
    }
    out.flush();
  }
Exemple #3
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 /** Adds the tagging with count to the data structures in this Lexicon. */
 protected void addTagging(boolean seen, IntTaggedWord itw, double count) {
   if (seen) {
     seenCounter.incrementCount(itw, count);
     if (itw.tag() == nullTag) {
       words.add(itw);
     } else if (itw.word() == nullWord) {
       tags.add(itw);
     } else {
       // rules.add(itw);
     }
   } else {
     uwModel.addTagging(seen, itw, count);
     // if (itw.tag() == nullTag) {
     // sigs.add(itw);
     // }
   }
 }
Exemple #4
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  protected void initRulesWithWord() {
    if (testOptions.verbose || DEBUG_LEXICON) {
      System.err.print("\nInitializing lexicon scores ... ");
    }
    // int numWords = words.size()+sigs.size()+1;
    int unkWord = wordIndex.indexOf(UNKNOWN_WORD, true);
    int numWords = wordIndex.size();
    rulesWithWord = new List[numWords];
    for (int w = 0; w < numWords; w++) {
      rulesWithWord[w] = new ArrayList<IntTaggedWord>(1); // most have 1 or 2
      // items in them
    }
    // for (Iterator ruleI = rules.iterator(); ruleI.hasNext();) {
    tags = new HashSet<IntTaggedWord>();
    for (IntTaggedWord iTW : seenCounter.keySet()) {
      if (iTW.word() == nullWord && iTW.tag() != nullTag) {
        tags.add(iTW);
      }
    }

    // tags for unknown words
    if (DEBUG_LEXICON) {
      System.err.println(
          "Lexicon initializing tags for UNKNOWN WORD ("
              + Lexicon.UNKNOWN_WORD
              + ", "
              + unkWord
              + ')');
    }
    if (DEBUG_LEXICON) System.err.println("unSeenCounter is: " + uwModel.unSeenCounter());
    if (DEBUG_LEXICON)
      System.err.println(
          "Train.openClassTypesThreshold is " + trainOptions.openClassTypesThreshold);
    for (IntTaggedWord iT : tags) {
      if (DEBUG_LEXICON)
        System.err.println("Entry for " + iT + " is " + uwModel.unSeenCounter().getCount(iT));
      double types = uwModel.unSeenCounter().getCount(iT);
      if (types > trainOptions.openClassTypesThreshold) {
        // Number of types before it's treated as open class
        IntTaggedWord iTW = new IntTaggedWord(unkWord, iT.tag);
        rulesWithWord[iTW.word].add(iTW);
      }
    }
    if (testOptions.verbose || DEBUG_LEXICON) {
      System.err.print("The " + rulesWithWord[unkWord].size() + " open class tags are: [");
      for (IntTaggedWord item : rulesWithWord[unkWord]) {
        System.err.print(" " + tagIndex.get(item.tag()));
        if (DEBUG_LEXICON) {
          IntTaggedWord iTprint = new IntTaggedWord(nullWord, item.tag);
          System.err.print(
              " (tag "
                  + item.tag()
                  + ", type count is "
                  + uwModel.unSeenCounter().getCount(iTprint)
                  + ')');
        }
      }
      System.err.println(" ] ");
    }

    for (IntTaggedWord iTW : seenCounter.keySet()) {
      if (iTW.tag() != nullTag && iTW.word() != nullWord) {
        rulesWithWord[iTW.word].add(iTW);
      }
    }
  }
  /**
   * Do max language model markov segmentation. Note that this algorithm inherently tags words as it
   * goes, but that we throw away the tags in the final result so that the segmented words are
   * untagged. (Note: for a couple of years till Aug 2007, a tagged result was returned, but this
   * messed up the parser, because it could use no tagging but the given tagging, which often wasn't
   * very good. Or in particular it was a subcategorized tagging which never worked with the current
   * forceTags option which assumes that gold taggings are inherently basic taggings.)
   *
   * @param s A String to segment
   * @return The list of segmented words.
   */
  private ArrayList<HasWord> segmentWordsWithMarkov(String s) {
    int length = s.length();
    //    Set<String> POSes = (Set<String>) POSDistribution.keySet();  // 1.5
    int numTags = POSes.size();
    // score of span with initial word of this tag
    double[][][] scores = new double[length][length + 1][numTags];
    // best (length of) first word for this span with this tag
    int[][][] splitBacktrace = new int[length][length + 1][numTags];
    // best tag for second word over this span, if first is this tag
    int[][][] POSbacktrace = new int[length][length + 1][numTags];
    for (int i = 0; i < length; i++) {
      for (int j = 0; j < length + 1; j++) {
        Arrays.fill(scores[i][j], Double.NEGATIVE_INFINITY);
      }
    }
    // first fill in word probabilities
    for (int diff = 1; diff <= 10; diff++) {
      for (int start = 0; start + diff <= length; start++) {
        int end = start + diff;
        StringBuilder wordBuf = new StringBuilder();
        for (int pos = start; pos < end; pos++) {
          wordBuf.append(s.charAt(pos));
        }
        String word = wordBuf.toString();
        for (String tag : POSes) {
          IntTaggedWord itw = new IntTaggedWord(word, tag, wordIndex, tagIndex);
          double score = lex.score(itw, 0, word, null);
          if (start == 0) {
            score += Math.log(initialPOSDist.probabilityOf(tag));
          }
          scores[start][end][itw.tag()] = score;
          splitBacktrace[start][end][itw.tag()] = end;
        }
      }
    }
    // now fill in word combination probabilities
    for (int diff = 2; diff <= length; diff++) {
      for (int start = 0; start + diff <= length; start++) {
        int end = start + diff;
        for (int split = start + 1; split < end && split - start <= 10; split++) {
          for (String tag : POSes) {
            int tagNum = tagIndex.indexOf(tag, true);
            if (splitBacktrace[start][split][tagNum] != split) {
              continue;
            }
            Distribution<String> rTagDist = markovPOSDists.get(tag);
            if (rTagDist == null) {
              continue; // this happens with "*" POS
            }
            for (String rTag : POSes) {
              int rTagNum = tagIndex.indexOf(rTag, true);
              double newScore =
                  scores[start][split][tagNum]
                      + scores[split][end][rTagNum]
                      + Math.log(rTagDist.probabilityOf(rTag));
              if (newScore > scores[start][end][tagNum]) {
                scores[start][end][tagNum] = newScore;
                splitBacktrace[start][end][tagNum] = split;
                POSbacktrace[start][end][tagNum] = rTagNum;
              }
            }
          }
        }
      }
    }
    int nextPOS = ArrayMath.argmax(scores[0][length]);
    ArrayList<HasWord> words = new ArrayList<HasWord>();

    int start = 0;
    while (start < length) {
      int split = splitBacktrace[start][length][nextPOS];
      StringBuilder wordBuf = new StringBuilder();
      for (int i = start; i < split; i++) {
        wordBuf.append(s.charAt(i));
      }
      String word = wordBuf.toString();
      // String tag = tagIndex.get(nextPOS);
      // words.add(new TaggedWord(word, tag));
      words.add(new Word(word));
      if (split < length) {
        nextPOS = POSbacktrace[start][length][nextPOS];
      }
      start = split;
    }

    return words;
  }
  // CDM 2007: I wonder what this does differently from segmentWordsWithMarkov???
  private ArrayList<TaggedWord> basicSegmentWords(String s) {
    int length = s.length();
    //    Set<String> POSes = (Set<String>) POSDistribution.keySet();  // 1.5
    // best score of span
    double[][] scores = new double[length][length + 1];
    // best (last index of) first word for this span
    int[][] splitBacktrace = new int[length][length + 1];
    // best tag for word over this span
    int[][] POSbacktrace = new int[length][length + 1];
    for (int i = 0; i < length; i++) {
      Arrays.fill(scores[i], Double.NEGATIVE_INFINITY);
    }
    // first fill in word probabilities
    for (int diff = 1; diff <= 10; diff++) {
      for (int start = 0; start + diff <= length; start++) {
        int end = start + diff;
        StringBuilder wordBuf = new StringBuilder();
        for (int pos = start; pos < end; pos++) {
          wordBuf.append(s.charAt(pos));
        }
        String word = wordBuf.toString();
        //        for (String tag : POSes) {  // 1.5
        for (Iterator<String> iter = POSes.iterator(); iter.hasNext(); ) {
          String tag = iter.next();
          IntTaggedWord itw = new IntTaggedWord(word, tag, wordIndex, tagIndex);
          double newScore =
              lex.score(itw, 0, word, null) + Math.log(lex.getPOSDistribution().probabilityOf(tag));
          if (newScore > scores[start][end]) {
            scores[start][end] = newScore;
            splitBacktrace[start][end] = end;
            POSbacktrace[start][end] = itw.tag();
          }
        }
      }
    }
    // now fill in word combination probabilities
    for (int diff = 2; diff <= length; diff++) {
      for (int start = 0; start + diff <= length; start++) {
        int end = start + diff;
        for (int split = start + 1; split < end && split - start <= 10; split++) {
          if (splitBacktrace[start][split] != split) {
            continue; // only consider words on left
          }
          double newScore = scores[start][split] + scores[split][end];
          if (newScore > scores[start][end]) {
            scores[start][end] = newScore;
            splitBacktrace[start][end] = split;
          }
        }
      }
    }

    List<TaggedWord> words = new ArrayList<TaggedWord>();
    int start = 0;
    while (start < length) {
      int end = splitBacktrace[start][length];
      StringBuilder wordBuf = new StringBuilder();
      for (int pos = start; pos < end; pos++) {
        wordBuf.append(s.charAt(pos));
      }
      String word = wordBuf.toString();
      String tag = tagIndex.get(POSbacktrace[start][end]);

      words.add(new TaggedWord(word, tag));
      start = end;
    }

    return new ArrayList<TaggedWord>(words);
  }