opennlp.tools.namefind.NameFinderME Maven / Gradle / Ivy
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* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package opennlp.tools.namefind;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import opennlp.tools.ml.BeamSearch;
import opennlp.tools.ml.EventModelSequenceTrainer;
import opennlp.tools.ml.EventTrainer;
import opennlp.tools.ml.SequenceTrainer;
import opennlp.tools.ml.TrainerFactory;
import opennlp.tools.ml.TrainerFactory.TrainerType;
import opennlp.tools.ml.model.Event;
import opennlp.tools.ml.model.MaxentModel;
import opennlp.tools.ml.model.SequenceClassificationModel;
import opennlp.tools.ml.perceptron.PerceptronTrainer;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.Sequence;
import opennlp.tools.util.SequenceCodec;
import opennlp.tools.util.SequenceValidator;
import opennlp.tools.util.Span;
import opennlp.tools.util.TrainingParameters;
import opennlp.tools.util.featuregen.AdaptiveFeatureGenerator;
import opennlp.tools.util.featuregen.AdditionalContextFeatureGenerator;
import opennlp.tools.util.featuregen.GeneratorFactory;
import opennlp.tools.util.featuregen.WindowFeatureGenerator;
/**
* Class for creating a maximum-entropy-based name finder.
*/
public class NameFinderME implements TokenNameFinder {
private static String[][] EMPTY = new String[0][0];
public static final int DEFAULT_BEAM_SIZE = 3;
private static final Pattern typedOutcomePattern = Pattern.compile("(.+)-\\w+");
public static final String START = "start";
public static final String CONTINUE = "cont";
public static final String OTHER = "other";
private SequenceCodec seqCodec = new BioCodec();
protected SequenceClassificationModel model;
protected NameContextGenerator contextGenerator;
private Sequence bestSequence;
private AdditionalContextFeatureGenerator additionalContextFeatureGenerator
= new AdditionalContextFeatureGenerator();
private SequenceValidator sequenceValidator;
public NameFinderME(TokenNameFinderModel model) {
TokenNameFinderFactory factory = model.getFactory();
seqCodec = factory.createSequenceCodec();
sequenceValidator = seqCodec.createSequenceValidator();
this.model = model.getNameFinderSequenceModel();
contextGenerator = factory.createContextGenerator();
// TODO: We should deprecate this. And come up with a better solution!
contextGenerator.addFeatureGenerator(
new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
}
private static AdaptiveFeatureGenerator createFeatureGenerator(
byte[] generatorDescriptor, final Map resources)
throws IOException {
AdaptiveFeatureGenerator featureGenerator;
if (generatorDescriptor != null) {
featureGenerator = GeneratorFactory.create(new ByteArrayInputStream(
generatorDescriptor), key -> {
if (resources != null) {
return resources.get(key);
}
return null;
});
} else {
featureGenerator = null;
}
return featureGenerator;
}
public Span[] find(String[] tokens) {
return find(tokens, EMPTY);
}
/**
* Generates name tags for the given sequence, typically a sentence, returning
* token spans for any identified names.
*
* @param tokens an array of the tokens or words of the sequence, typically a sentence.
* @param additionalContext features which are based on context outside of the
* sentence but which should also be used.
*
* @return an array of spans for each of the names identified.
*/
public Span[] find(String[] tokens, String[][] additionalContext) {
additionalContextFeatureGenerator.setCurrentContext(additionalContext);
bestSequence = model.bestSequence(tokens, additionalContext, contextGenerator, sequenceValidator);
List c = bestSequence.getOutcomes();
contextGenerator.updateAdaptiveData(tokens, c.toArray(new String[c.size()]));
Span[] spans = seqCodec.decode(c);
spans = setProbs(spans);
return spans;
}
/**
* Forgets all adaptive data which was collected during previous calls to one
* of the find methods.
*
* This method is typical called at the end of a document.
*/
public void clearAdaptiveData() {
contextGenerator.clearAdaptiveData();
}
/**
* Populates the specified array with the probabilities of the last decoded
* sequence. The sequence was determined based on the previous call to
* chunk. The specified array should be at least as large as the
* number of tokens in the previous call to chunk.
*
* @param probs An array used to hold the probabilities of the last decoded
* sequence.
*/
public void probs(double[] probs) {
bestSequence.getProbs(probs);
}
/**
* Returns an array with the probabilities of the last decoded sequence. The
* sequence was determined based on the previous call to chunk.
*
* @return An array with the same number of probabilities as tokens were sent
* to chunk when it was last called.
*/
public double[] probs() {
return bestSequence.getProbs();
}
/**
* sets the probs for the spans
*
* @param spans
* @return
*/
private Span[] setProbs(Span[] spans) {
double[] probs = probs(spans);
if (probs != null) {
for (int i = 0; i < probs.length; i++) {
double prob = probs[i];
spans[i] = new Span(spans[i], prob);
}
}
return spans;
}
/**
* Returns an array of probabilities for each of the specified spans which is
* the arithmetic mean of the probabilities for each of the outcomes which
* make up the span.
*
* @param spans The spans of the names for which probabilities are desired.
*
* @return an array of probabilities for each of the specified spans.
*/
public double[] probs(Span[] spans) {
double[] sprobs = new double[spans.length];
double[] probs = bestSequence.getProbs();
for (int si = 0; si < spans.length; si++) {
double p = 0;
for (int oi = spans[si].getStart(); oi < spans[si].getEnd(); oi++) {
p += probs[oi];
}
p /= spans[si].length();
sprobs[si] = p;
}
return sprobs;
}
public static TokenNameFinderModel train(String languageCode, String type,
ObjectStream samples, TrainingParameters trainParams,
TokenNameFinderFactory factory) throws IOException {
trainParams.putIfAbsent(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE);
trainParams.putIfAbsent(TrainingParameters.CUTOFF_PARAM, 0);
trainParams.putIfAbsent(TrainingParameters.ITERATIONS_PARAM, 300);
int beamSize = trainParams.getIntParameter(BeamSearch.BEAM_SIZE_PARAMETER,
NameFinderME.DEFAULT_BEAM_SIZE);
Map manifestInfoEntries = new HashMap<>();
MaxentModel nameFinderModel = null;
SequenceClassificationModel seqModel = null;
TrainerType trainerType = TrainerFactory.getTrainerType(trainParams);
if (TrainerType.EVENT_MODEL_TRAINER.equals(trainerType)) {
ObjectStream eventStream = new NameFinderEventStream(samples, type,
factory.createContextGenerator(), factory.createSequenceCodec());
EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams, manifestInfoEntries);
nameFinderModel = trainer.train(eventStream);
} // TODO: Maybe it is not a good idea, that these two don't use the context generator ?!
// These also don't use the sequence codec ?!
else if (TrainerType.EVENT_MODEL_SEQUENCE_TRAINER.equals(trainerType)) {
NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, factory.createContextGenerator());
EventModelSequenceTrainer trainer = TrainerFactory.getEventModelSequenceTrainer(
trainParams, manifestInfoEntries);
nameFinderModel = trainer.train(ss);
} else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
SequenceTrainer trainer = TrainerFactory.getSequenceModelTrainer(
trainParams, manifestInfoEntries);
NameSampleSequenceStream ss =
new NameSampleSequenceStream(samples, factory.createContextGenerator(), false);
seqModel = trainer.train(ss);
} else {
throw new IllegalStateException("Unexpected trainer type!");
}
if (seqModel != null) {
return new TokenNameFinderModel(languageCode, seqModel, factory.getFeatureGenerator(),
factory.getResources(), manifestInfoEntries, factory.getSequenceCodec(), factory);
} else {
return new TokenNameFinderModel(languageCode, nameFinderModel, beamSize, factory.getFeatureGenerator(),
factory.getResources(), manifestInfoEntries, factory.getSequenceCodec(), factory);
}
}
/**
* Gets the name type from the outcome
*
* @param outcome the outcome
* @return the name type, or null if not set
*/
static String extractNameType(String outcome) {
Matcher matcher = typedOutcomePattern.matcher(outcome);
if (matcher.matches()) {
return matcher.group(1);
}
return null;
}
/**
* Removes spans with are intersecting or crossing in anyway.
*
*
* The following rules are used to remove the spans:
* Identical spans: The first span in the array after sorting it remains
* Intersecting spans: The first span after sorting remains
* Contained spans: All spans which are contained by another are removed
*
* @param spans
*
* @return non-overlapping spans
*/
public static Span[] dropOverlappingSpans(Span[] spans) {
List sortedSpans = new ArrayList<>(spans.length);
Collections.addAll(sortedSpans, spans);
Collections.sort(sortedSpans);
Iterator it = sortedSpans.iterator();
Span lastSpan = null;
while (it.hasNext()) {
Span span = it.next();
if (lastSpan != null) {
if (lastSpan.intersects(span)) {
it.remove();
span = lastSpan;
}
}
lastSpan = span;
}
return sortedSpans.toArray(new Span[sortedSpans.size()]);
}
}