Package edu.stanford.nlp.ling

Examples of edu.stanford.nlp.ling.CoreLabel.word()

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      feat.incrementCount("PREV-" + "TAG-" + lj.tag());
    }

    for (int j = i + 1; j < sent.length && j <= i + window; j++) {
      CoreLabel lj = sent[j];
      feat.incrementCount("NEXT-" + "WORD-" + lj.word());
      feat.incrementCount("NEXT-" + "LEMMA-" + lj.lemma());
      feat.incrementCount("NEXT-" + "TAG-" + lj.tag());
    }

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        if (chooseThis) {
          chosen.add(new Pair<String, Integer>(en.getKey(), i));

          Counter<ScorePhraseMeasures> feat = null;
          if (forLearningPattern) {
            feat = getPhraseFeaturesForPattern(label, l.word());
          } else {
            feat = getFeatures(label, l.word(), wordsPatExtracted.getCounter(l.word()), allSelectedPatterns);
          }
          RVFDatum<String, ScorePhraseMeasures> datum = new RVFDatum<String, ScorePhraseMeasures>(feat, datumlabel.toString());
          dataset.add(datum);
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          Counter<ScorePhraseMeasures> feat = null;
          if (forLearningPattern) {
            feat = getPhraseFeaturesForPattern(label, l.word());
          } else {
            feat = getFeatures(label, l.word(), wordsPatExtracted.getCounter(l.word()), allSelectedPatterns);
          }
          RVFDatum<String, ScorePhraseMeasures> datum = new RVFDatum<String, ScorePhraseMeasures>(feat, datumlabel.toString());
          dataset.add(datum);
        }
      }
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          Counter<ScorePhraseMeasures> feat = null;
          if (forLearningPattern) {
            feat = getPhraseFeaturesForPattern(label, l.word());
          } else {
            feat = getFeatures(label, l.word(), wordsPatExtracted.getCounter(l.word()), allSelectedPatterns);
          }
          RVFDatum<String, ScorePhraseMeasures> datum = new RVFDatum<String, ScorePhraseMeasures>(feat, datumlabel.toString());
          dataset.add(datum);
        }
      }
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        boolean chooseThis = false;
        boolean ignoreclass = false;
        Boolean datumlabel = false;
        if (l.get(answerClass).equals(answerLabel)) {
          continue;
        } else if ((ignoreclass || negativeWords.contains(l.word().toLowerCase())) && getRandomBoolean(rneg, perSelectNeg)) {
          chooseThis = true;
          datumlabel = false;
          numneg++;
        } else if (getRandomBoolean(r, perSelectRand)) {
          chooseThis = true;
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          chooseThis = false;
        if (chooseThis) {
          chosen.add(new Pair<String, Integer>(en.getKey(), i));
          Counter<ScorePhraseMeasures> feat = null;
          if (forLearningPattern) {
            feat = getPhraseFeaturesForPattern(label, l.word());
          } else {
            feat = getFeatures(label, l.word(), wordsPatExtracted.getCounter(l.word()), allSelectedPatterns);
          }
          RVFDatum<String, ScorePhraseMeasures> datum = new RVFDatum<String, ScorePhraseMeasures>(feat, datumlabel.toString());
          dataset.add(datum);
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          chosen.add(new Pair<String, Integer>(en.getKey(), i));
          Counter<ScorePhraseMeasures> feat = null;
          if (forLearningPattern) {
            feat = getPhraseFeaturesForPattern(label, l.word());
          } else {
            feat = getFeatures(label, l.word(), wordsPatExtracted.getCounter(l.word()), allSelectedPatterns);
          }
          RVFDatum<String, ScorePhraseMeasures> datum = new RVFDatum<String, ScorePhraseMeasures>(feat, datumlabel.toString());
          dataset.add(datum);
        }
      }
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          chosen.add(new Pair<String, Integer>(en.getKey(), i));
          Counter<ScorePhraseMeasures> feat = null;
          if (forLearningPattern) {
            feat = getPhraseFeaturesForPattern(label, l.word());
          } else {
            feat = getFeatures(label, l.word(), wordsPatExtracted.getCounter(l.word()), allSelectedPatterns);
          }
          RVFDatum<String, ScorePhraseMeasures> datum = new RVFDatum<String, ScorePhraseMeasures>(feat, datumlabel.toString());
          dataset.add(datum);
        }
      }
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            if (!containsStop || !removeStopWordsFromSelectedPhrases) {
             
              if (label == null || l.get(constVars.getAnswerClass().get(label)) == null || !l.get(constVars.getAnswerClass().get(label)).equals(label.toString())) {
                useWordNotLabeled = true;
              }
              phrase += " " + l.word();
              phraseLemma += " " + l.lemma();
              addedindices[i-s] = true;
            }
          }
        }
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    while (wordIter == null || ! wordIter.hasNext()) {
      if ( ! tok.hasNext()) {
        return null;
      }
      CoreLabel token = tok.next();
      String s = token.word();
      if (s == null) {
        return null;
      }
      if (s.equals(WhitespaceLexer.NEWLINE)) {
        // if newlines were significant, we should make sure to return
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