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/*
* Copyright (c) 2012, Regents of the University of Colorado
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
* Neither the name of the University of Colorado at Boulder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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package org.cleartk.examples.chunking;
import java.util.ArrayList;
import java.util.List;
import org.apache.uima.UimaContext;
import org.apache.uima.analysis_engine.AnalysisEngineProcessException;
import org.apache.uima.jcas.JCas;
import org.apache.uima.resource.ResourceInitializationException;
import org.cleartk.classifier.CleartkSequenceAnnotator;
import org.cleartk.classifier.Feature;
import org.cleartk.classifier.Instances;
import org.cleartk.classifier.chunking.BIOChunking;
import org.cleartk.classifier.feature.extractor.CleartkExtractor;
import org.cleartk.classifier.feature.extractor.CleartkExtractor.Following;
import org.cleartk.classifier.feature.extractor.CleartkExtractor.Preceding;
import org.cleartk.classifier.feature.extractor.simple.CharacterCategoryPatternExtractor;
import org.cleartk.classifier.feature.extractor.simple.CharacterCategoryPatternExtractor.PatternType;
import org.cleartk.classifier.feature.extractor.simple.CombinedExtractor;
import org.cleartk.classifier.feature.extractor.simple.CoveredTextExtractor;
import org.cleartk.classifier.feature.extractor.simple.SimpleFeatureExtractor;
import org.cleartk.classifier.feature.extractor.simple.TypePathExtractor;
import org.cleartk.ne.type.NamedEntityMention;
import org.cleartk.token.type.Sentence;
import org.cleartk.token.type.Token;
import org.uimafit.util.JCasUtil;
/**
* This is the most important class in the named entity chunking example -- it demonstrates how to
* write a ClearTK annotator that creates NamedEntityMention annotations by classifying Token
* annotations.
*
*
* Copyright (c) 2012, Regents of the University of Colorado
* All rights reserved.
*
* @author Steven Bethard
*/
public class NamedEntityChunker extends CleartkSequenceAnnotator {
private SimpleFeatureExtractor extractor;
private CleartkExtractor contextExtractor;
private BIOChunking chunking;
@Override
public void initialize(UimaContext context) throws ResourceInitializationException {
super.initialize(context);
// the token feature extractor: text, char pattern (uppercase, digits, etc.), and part-of-speech
this.extractor = new CombinedExtractor(
new CoveredTextExtractor(),
new CharacterCategoryPatternExtractor(PatternType.REPEATS_MERGED),
new TypePathExtractor(Token.class, "pos"));
// the context feature extractor: the features above for the 3 preceding and 3 following tokens
this.contextExtractor = new CleartkExtractor(
Token.class,
this.extractor,
new Preceding(3),
new Following(3));
// the chunking definition: Tokens will be combined to form NamedEntityMentions, with labels
// from the "mentionType" attribute so that we get B-location, I-person, etc.
this.chunking = new BIOChunking(
Token.class,
NamedEntityMention.class,
"mentionType");
}
@Override
public void process(JCas jCas) throws AnalysisEngineProcessException {
for (Sentence sentence : JCasUtil.select(jCas, Sentence.class)) {
// extract features for each token in the sentence
List tokens = JCasUtil.selectCovered(jCas, Token.class, sentence);
List> featureLists = new ArrayList>();
for (Token token : tokens) {
List features = new ArrayList();
features.addAll(this.extractor.extract(jCas, token));
features.addAll(this.contextExtractor.extract(jCas, token));
featureLists.add(features);
}
// during training, convert NamedEntityMentions in the CAS into expected classifier outcomes
if (this.isTraining()) {
// extract the gold (human annotated) NamedEntityMention annotations
List namedEntityMentions = JCasUtil.selectCovered(
jCas,
NamedEntityMention.class,
sentence);
// convert the NamedEntityMention annotations into token-level BIO outcome labels
List outcomes = this.chunking.createOutcomes(jCas, tokens, namedEntityMentions);
// write the features and outcomes as training instances
this.dataWriter.write(Instances.toInstances(outcomes, featureLists));
}
// during classification, convert classifier outcomes into NamedEntityMentions in the CAS
else {
// get the predicted BIO outcome labels from the classifier
List outcomes = this.classifier.classify(featureLists);
// create the NamedEntityMention annotations in the CAS
this.chunking.createChunks(jCas, tokens, outcomes);
}
}
}
}
