Package opennlp.model

Examples of opennlp.model.Event

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   */
  public void trainModel() throws IOException {
    if (debugOn) {
      FileWriter writer = new FileWriter(modelName+".events");
      for (Iterator<Event> ei=events.iterator();ei.hasNext();) {
        Event e = ei.next();
        writer.write(e.toString()+"\n");
      }
      writer.close();
    }
    (new SuffixSensitiveGISModelWriter(GIS.trainModel(
        new CollectionEventStream(events),100,10),
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      // it is safe to pass the tags as previous tags because
      // the context generator does not look for non predicted tags
      String[] context = cg.getContext(i, sentence, tags, null);

      events[i] = new Event(tags[i], context);
    }
    Sequence<NameSample> sequence = new Sequence<NameSample>(events,sample);
    return sequence;
  }
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    return features;
  }

  private void addEvent(String outcome, Context np1) {
    List<String> feats = getFeatures(np1);
    events.add(new Event(outcome, feats.toArray(new String[feats.size()])));
  }
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  public void trainModel() throws IOException {
    if (debugOn) {
      FileWriter writer = new FileWriter(modelName+".events");
      for (Iterator<Event> ei=events.iterator();ei.hasNext();) {
        Event e = ei.next();
        writer.write(e.toString()+"\n");
      }
      writer.close();
    }
    new SuffixSensitiveGISModelWriter(
        GIS.trainModel(
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  }

  public static List<Event> generateEvents(String[] sentence, String[] outcomes, NameContextGenerator cg) {
    List<Event> events = new ArrayList<Event>(outcomes.length);
    for (int i = 0; i < outcomes.length; i++) {
      events.add(new Event(outcomes[i], cg.getContext(i, sentence, outcomes,null)));
    }
   
    cg.updateAdaptiveData(sentence, outcomes);

    return events;
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            if (debugOn) {
              System.err.println(this +".retain: " + mention.getId() + " " + mention.toText() + " -> " + entityMention.getId() + " " + cde);
            }
            if (mention.getId() != -1 && entityMention.getId() == mention.getId()) {
              referentFound = true;
              events.add(new Event(SAME, features.toArray(new String[features.size()])));
              de = cde;
              //System.err.println("MaxentResolver.retain: resolved at "+ei);
              distances.add(ei);
            }
            else if (!pairedSampleSelection || (!nonReferentFound && useAsDifferentExample)) {
              nonReferentFound = true;
              events.add(new Event(DIFF, features.toArray(new String[features.size()])));
            }
          //}
        }
        if (pairedSampleSelection && referentFound && nonReferentFound) {
          break;
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    if (ResolverMode.TRAIN == mode) {
      if (debugOn) {
        System.err.println(this +" referential");
        FileWriter writer = new FileWriter(modelName+".events");
        for (Iterator<Event> ei=events.iterator();ei.hasNext();) {
          Event e = ei.next();
          writer.write(e.toString()+"\n");
        }
        writer.close();
      }
      (new SuffixSensitiveGISModelWriter(GIS.trainModel(new CollectionEventStream(events),100,10),new File(modelName+modelExtension))).persist();
      nonReferentialResolver.train();
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      // it is safe to pass the tags as previous tags because
      // the context generator does not look for non predicted tags
      String[] context = cg.getContext(i, sentence, tags, null);

      events[i] = new Event(tags[i], context);
    }
    Sequence<POSSample> sequence = new Sequence<POSSample>(events,sample);
    return sequence;
  }
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    return features;
  }

  private void addEvent(String outcome, Context np1) {
    List<String> feats = getFeatures(np1);
    events.add(new Event(outcome, feats.toArray(new String[feats.size()])));
  }
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          outcome = AbstractBottomUpParser.CONT + type;
        }
        //System.err.println("parserEventStream.addParseEvents: chunks["+ci+"]="+c+" label="+outcome+" bcg="+bcg);
        c.setLabel(outcome);
        if (etype == ParserEventTypeEnum.BUILD) {
          parseEvents.add(new Event(outcome, bcg.getContext(chunks, ci)));
        }
        int start = ci - 1;
        while (start >= 0 && chunks[start].getParent() == parent) {
          start--;
        }
        if (lastChild(c, parent)) {
          if (etype == ParserEventTypeEnum.CHECK) {
            parseEvents.add(new Event(Parser.COMPLETE, kcg.getContext( chunks, type, start + 1, ci)));
          }
          //perform reduce
          int reduceStart = ci;
          while (reduceStart >=0 && chunks[reduceStart].getParent() == parent) {
            reduceStart--;
          }
          reduceStart++;
          chunks = reduceChunks(chunks,ci,parent);
          ci=reduceStart-1; //ci will be incremented at end of loop
        }
        else {
          if (etype == ParserEventTypeEnum.CHECK) {
            parseEvents.add(new Event(Parser.INCOMPLETE, kcg.getContext(chunks, type, start + 1, ci)));
          }
        }
      }
      ci++;
    }
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Related Classes of opennlp.model.Event

  • com.tamingtext.qa.AnswerTypeEventStream
  • edu.washington.cs.knowitall.extractor.conf.opennlp.OpenNlpDataSet
  • opennlp.maxent.BasicEventStream
  • opennlp.maxent.quasinewton.QNTrainerTest
  • opennlp.maxent.RealBasicEventStream
  • opennlp.model.HashSumEventStream
  • opennlp.PrepAttachDataUtil
  • opennlp.tools.chunker.ChunkerEventStream
  • opennlp.tools.coref.resolver.DefaultNonReferentialResolver
  • opennlp.tools.coref.resolver.MaxentResolver
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