/*
* 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.io.ObjectStreamException;
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.maxent.GIS;
import opennlp.maxent.GISModel;
import opennlp.model.AbstractModel;
import opennlp.model.EventStream;
import opennlp.model.MaxentModel;
import opennlp.model.TrainUtil;
import opennlp.model.TwoPassDataIndexer;
import opennlp.tools.util.BeamSearch;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.Sequence;
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.BigramNameFeatureGenerator;
import opennlp.tools.util.featuregen.CachedFeatureGenerator;
import opennlp.tools.util.featuregen.FeatureGeneratorResourceProvider;
import opennlp.tools.util.featuregen.GeneratorFactory;
import opennlp.tools.util.featuregen.OutcomePriorFeatureGenerator;
import opennlp.tools.util.featuregen.PreviousMapFeatureGenerator;
import opennlp.tools.util.featuregen.SentenceFeatureGenerator;
import opennlp.tools.util.featuregen.TokenClassFeatureGenerator;
import opennlp.tools.util.featuregen.TokenFeatureGenerator;
import opennlp.tools.util.featuregen.WindowFeatureGenerator;
import opennlp.tools.util.model.ModelUtil;
/**
* 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";
protected MaxentModel model;
protected NameContextGenerator contextGenerator;
private Sequence bestSequence;
private BeamSearch<String> beam;
private AdditionalContextFeatureGenerator additionalContextFeatureGenerator =
new AdditionalContextFeatureGenerator();
public NameFinderME(TokenNameFinderModel model) {
this(model, DEFAULT_BEAM_SIZE);
}
/**
* Initializes the name finder with the specified model.
*
* @param model
* @param beamSize
*/
public NameFinderME(TokenNameFinderModel model, AdaptiveFeatureGenerator generator, int beamSize,
SequenceValidator<String> sequenceValidator) {
this.model = model.getNameFinderModel();
// If generator is provided always use that one
if (generator != null) {
contextGenerator = new DefaultNameContextGenerator(generator);
}
else {
// If model has a generator use that one, otherwise create default
AdaptiveFeatureGenerator featureGenerator = model.createFeatureGenerators();
if (featureGenerator == null)
featureGenerator = createFeatureGenerator();
contextGenerator = new DefaultNameContextGenerator(featureGenerator);
}
contextGenerator.addFeatureGenerator(
new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
if (sequenceValidator == null)
sequenceValidator = new NameFinderSequenceValidator();
beam = new BeamSearch<String>(beamSize, contextGenerator, this.model,
sequenceValidator, beamSize);
}
public NameFinderME(TokenNameFinderModel model, AdaptiveFeatureGenerator generator, int beamSize) {
this(model, generator, beamSize, null);
}
public NameFinderME(TokenNameFinderModel model, int beamSize) {
this(model, null, beamSize);
}
/**
* Creates a new name finder with the specified model.
*
* @param mod The model to be used to find names.
*
* @deprecated Use the new model API!
*/
@Deprecated
public NameFinderME(MaxentModel mod) {
this(mod, new DefaultNameContextGenerator(), DEFAULT_BEAM_SIZE);
}
/**
* Creates a new name finder with the specified model and context generator.
*
* @param mod The model to be used to find names.
* @param cg The context generator to be used with this name finder.
*/
@Deprecated
public NameFinderME(MaxentModel mod, NameContextGenerator cg) {
this(mod, cg, DEFAULT_BEAM_SIZE);
}
/**
* Creates a new name finder with the specified model and context generator.
*
* @param mod The model to be used to find names.
* @param cg The context generator to be used with this name finder.
* @param beamSize The size of the beam to be used in decoding this model.
*/
@Deprecated
public NameFinderME(MaxentModel mod, NameContextGenerator cg, int beamSize) {
model = mod;
contextGenerator = cg;
contextGenerator.addFeatureGenerator(new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
beam = new BeamSearch<String>(beamSize, cg, mod,
new NameFinderSequenceValidator(), beamSize);
}
private static AdaptiveFeatureGenerator createFeatureGenerator() {
return new CachedFeatureGenerator(
new AdaptiveFeatureGenerator[]{
new WindowFeatureGenerator(new TokenFeatureGenerator(), 2, 2),
new WindowFeatureGenerator(new TokenClassFeatureGenerator(true), 2, 2),
new OutcomePriorFeatureGenerator(),
new PreviousMapFeatureGenerator(),
new BigramNameFeatureGenerator(),
new SentenceFeatureGenerator(true, false)
});
}
private static AdaptiveFeatureGenerator createFeatureGenerator(
byte[] generatorDescriptor, final Map<String, Object> resources)
throws IOException {
AdaptiveFeatureGenerator featureGenerator;
if (generatorDescriptor != null) {
featureGenerator = GeneratorFactory.create(new ByteArrayInputStream(
generatorDescriptor), new FeatureGeneratorResourceProvider() {
public Object getResource(String key) {
return resources.get(key);
}
});
} 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 = beam.bestSequence(tokens, additionalContext);
List<String> c = bestSequence.getOutcomes();
contextGenerator.updateAdaptiveData(tokens, c.toArray(new String[c.size()]));
int start = -1;
int end = -1;
List<Span> spans = new ArrayList<Span>(tokens.length);
for (int li = 0; li < c.size(); li++) {
String chunkTag = c.get(li);
if (chunkTag.endsWith(NameFinderME.START)) {
if (start != -1) {
spans.add(new Span(start, end, extractNameType(chunkTag)));
}
start = li;
end = li + 1;
}
else if (chunkTag.endsWith(NameFinderME.CONTINUE)) {
end = li + 1;
}
else if (chunkTag.endsWith(NameFinderME.OTHER)) {
if (start != -1) {
spans.add(new Span(start, end, extractNameType(c.get(li - 1))));
start = -1;
end = -1;
}
}
}
if (start != -1) {
spans.add(new Span(start, end, extractNameType(c.get(c.size() - 1))));
}
return spans.toArray(new Span[spans.size()]);
}
/**
* 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
* <code>chunk</code>. The specified array should be at least as large as
* the number of tokens in the previous call to <code>chunk</code>.
*
* @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 <code>chunk</code>.
*
* @return An array with the same number of probabilities as tokens were sent to <code>chunk</code>
* when it was last called.
*/
public double[] probs() {
return bestSequence.getProbs();
}
/**
* 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;
}
/**
* Trains a name finder model.
*
* @param languageCode
* the language of the training data
* @param type
* null or an override type for all types in the training data
* @param samples
* the training data
* @param trainParams
* machine learning train parameters
* @param generator
* null or the feature generator
* @param resources
* the resources for the name finder or null if none
*
* @return the newly trained model
*
* @throws IOException
*/
public static TokenNameFinderModel train(String languageCode, String type, ObjectStream<NameSample> samples,
TrainingParameters trainParams, AdaptiveFeatureGenerator generator, final Map<String, Object> resources) throws IOException {
Map<String, String> manifestInfoEntries = new HashMap<String, String>();
AdaptiveFeatureGenerator featureGenerator;
if (generator != null)
featureGenerator = generator;
else
featureGenerator = createFeatureGenerator();
AbstractModel nameFinderModel;
if (!TrainUtil.isSequenceTraining(trainParams.getSettings())) {
EventStream eventStream = new NameFinderEventStream(samples, type,
new DefaultNameContextGenerator(featureGenerator));
nameFinderModel = TrainUtil.train(eventStream, trainParams.getSettings(), manifestInfoEntries);
}
else {
NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, featureGenerator);
nameFinderModel = TrainUtil.train(ss, trainParams.getSettings(), manifestInfoEntries);
}
return new TokenNameFinderModel(languageCode, nameFinderModel,
resources, manifestInfoEntries);
}
/**
* Trains a name finder model.
*
* @param languageCode
* the language of the training data
* @param type
* null or an override type for all types in the training data
* @param samples
* the training data
* @param trainParams
* machine learning train parameters
* @param featureGeneratorBytes
* descriptor to configure the feature generation or null
* @param resources
* the resources for the name finder or null if none
*
* @return the newly trained model
*
* @throws IOException
*/
public static TokenNameFinderModel train(String languageCode, String type,
ObjectStream<NameSample> samples, TrainingParameters trainParams,
byte[] featureGeneratorBytes, final Map<String, Object> resources)
throws IOException {
TokenNameFinderModel model = train(languageCode, type, samples, trainParams,
createFeatureGenerator(featureGeneratorBytes, resources), resources);
// place the descriptor in the model
if (featureGeneratorBytes != null) {
model = model.updateFeatureGenerator(featureGeneratorBytes);
}
return model;
}
/**
* Trains a name finder model.
*
* @param languageCode the language of the training data
* @param type null or an override type for all types in the training data
* @param samples the training data
* @param iterations the number of iterations
* @param cutoff
* @param resources the resources for the name finder or null if none
*
* @return the newly trained model
*
* @throws IOException
* @throws ObjectStreamException
*/
public static TokenNameFinderModel train(String languageCode, String type, ObjectStream<NameSample> samples,
AdaptiveFeatureGenerator generator, final Map<String, Object> resources,
int iterations, int cutoff) throws IOException {
return train(languageCode, type, samples, ModelUtil.createTrainingParameters(iterations, cutoff),
generator, resources);
}
/**
* @deprecated use {@link #train(String, String, ObjectStream, TrainingParameters, AdaptiveFeatureGenerator, Map)}
* instead and pass in a TrainingParameters object.
*/
@Deprecated
public static TokenNameFinderModel train(String languageCode, String type, ObjectStream<NameSample> samples,
final Map<String, Object> resources, int iterations, int cutoff) throws IOException {
return train(languageCode, type, samples, (AdaptiveFeatureGenerator) null, resources, iterations, cutoff);
}
public static TokenNameFinderModel train(String languageCode, String type, ObjectStream<NameSample> samples,
final Map<String, Object> resources) throws IOException {
return NameFinderME.train(languageCode, type, samples, resources, 100, 5);
}
/**
* @deprecated use {@link #train(String, String, ObjectStream, TrainingParameters, byte[], Map)}
* instead and pass in a TrainingParameters object.
*/
@Deprecated
public static TokenNameFinderModel train(String languageCode, String type, ObjectStream<NameSample> samples,
byte[] generatorDescriptor, final Map<String, Object> resources,
int iterations, int cutoff) throws IOException {
// TODO: Pass in resource manager ...
AdaptiveFeatureGenerator featureGenerator = createFeatureGenerator(generatorDescriptor, resources);
TokenNameFinderModel model = train(languageCode, type, samples, featureGenerator,
resources, iterations, cutoff);
if (generatorDescriptor != null) {
model = model.updateFeatureGenerator(generatorDescriptor);
}
return model;
}
@Deprecated
public static GISModel train(EventStream es, int iterations, int cut) throws IOException {
return GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
}
/**
* Gets the name type from the outcome
* @param outcome the outcome
* @return the name type, or null if not set
*/
static final String extractNameType(String outcome) {
Matcher matcher = typedOutcomePattern.matcher(outcome);
if(matcher.matches()) {
String nameType = matcher.group(1);
return nameType;
}
return null;
}
/**
* Removes spans with are intersecting or crossing in anyway.
*
* <p>
* The following rules are used to remove the spans:<br>
* Identical spans: The first span in the array after sorting it remains<br>
* Intersecting spans: The first span after sorting remains<br>
* Contained spans: All spans which are contained by another are removed<br>
*
* @param spans
*
* @return non-overlapping spans
*/
public static Span[] dropOverlappingSpans(Span spans[]) {
List<Span> sortedSpans = new ArrayList<Span>(spans.length);
Collections.addAll(sortedSpans, spans);
Collections.sort(sortedSpans);
Iterator<Span> 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()]);
}
}