opennlp.tools.namefind.NameFinderME Maven / Gradle / Ivy
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* 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.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.AdditionalContextFeatureGenerator;
import opennlp.tools.util.featuregen.WindowFeatureGenerator;
/**
* A maximum-entropy-based {@link TokenNameFinder name finder} implementation.
*/
public class NameFinderME implements TokenNameFinder {
private static final 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 final SequenceCodec seqCodec;
protected final SequenceClassificationModel model;
protected final NameContextGenerator contextGenerator;
private Sequence bestSequence;
private final AdditionalContextFeatureGenerator additionalContextFeatureGenerator =
new AdditionalContextFeatureGenerator();
private final SequenceValidator sequenceValidator;
/**
* Initializes a {@link NameFinderME} with a {@link TokenNameFinderModel}.
*
* @param model The {@link TokenNameFinderModel} to initialize with.
*/
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));
}
@Override
public Span[] find(String[] tokens) {
return find(tokens, EMPTY);
}
/**
* Generates name tags for the given sequence, typically a sentence, returning
* {@link Span token spans} for any identified names.
*
* @param tokens An array of the tokens or words of a 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 {@link Span token 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[0]));
Span[] spans = seqCodec.decode(c);
spans = setProbs(spans);
return spans;
}
@Override
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
* {@link #find(String[])}. The specified array should be at least as large as the
* number of tokens in the previous call to {@link #find(String[])}.
*
* @param probs An array with the probabilities of the last decoded sequence.
*/
public void probs(double[] probs) {
bestSequence.getProbs(probs);
}
/**
* Retrieves the probabilities of the last decoded sequence. The
* sequence was determined based on the previous call to {@link #find(String[])}.
*
* @return An array with the same number of probabilities as tokens were sent
* to {@link #find(String[])} when it was last called.
*/
public double[] probs() {
return bestSequence.getProbs();
}
/**
* Sets probabilities for the spans.
*
* @param spans The {@link Span spans} to set probabilities.
*
* @return The {@link Span spans} with populated values.
*/
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;
}
/**
* Retrieves 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 {@link Span spans} of the names for which probabilities
* are requested.
*
* @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;
}
/**
* Starts a training of a {@link TokenNameFinderModel} with the given parameters.
*
* @param languageCode The ISO conform language code.
* @param type The type to use.
* @param samples The {@link ObjectStream} of {@link NameSample} used as input for training.
* @param params The {@link TrainingParameters} for the context of the training.
* @param factory The {@link TokenNameFinderFactory} for creating related objects defined
* via {@code params}.
*
* @return A valid, trained {@link TokenNameFinderModel} instance.
* @throws IOException Thrown if IO errors occurred during training.
*/
public static TokenNameFinderModel train(String languageCode, String type,
ObjectStream samples, TrainingParameters params,
TokenNameFinderFactory factory) throws IOException {
params.putIfAbsent(TrainingParameters.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE);
params.putIfAbsent(TrainingParameters.CUTOFF_PARAM, 0);
params.putIfAbsent(TrainingParameters.ITERATIONS_PARAM, 300);
int beamSize = params.getIntParameter(BeamSearch.BEAM_SIZE_PARAMETER, NameFinderME.DEFAULT_BEAM_SIZE);
Map manifestInfoEntries = new HashMap<>();
MaxentModel nameFinderModel = null;
SequenceClassificationModel seqModel = null;
TrainerType trainerType = TrainerFactory.getTrainerType(params);
if (TrainerType.EVENT_MODEL_TRAINER.equals(trainerType)) {
ObjectStream eventStream = new NameFinderEventStream(samples, type,
factory.createContextGenerator(), factory.createSequenceCodec());
EventTrainer trainer = TrainerFactory.getEventTrainer(params, 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(
params, manifestInfoEntries);
nameFinderModel = trainer.train(ss);
} else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
SequenceTrainer trainer = TrainerFactory.getSequenceModelTrainer(
params, 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);
}
}
/**
* Extracts the name type from the {@code outcome}.
*
* @param outcome The outcome
* @return The name type, or {@code null} if not set.
*/
static String extractNameType(String outcome) {
Matcher matcher = typedOutcomePattern.matcher(outcome);
if (matcher.matches()) {
return matcher.group(1);
}
return null;
}
/**
* Removes {@link Span spans} with are intersecting or crossing in any way.
*
*
* 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 The input {@link Span spans}.
*
* @return The resulting non-overlapping {@link Span 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[0]);
}
}