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.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStreamReader;
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.TwoPassDataIndexer;
import opennlp.tools.util.BeamSearch;
import opennlp.tools.util.HashSumEventStream;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.PlainTextByLineStream;
import opennlp.tools.util.Sequence;
import opennlp.tools.util.SequenceValidator;
import opennlp.tools.util.Span;
import opennlp.tools.util.featuregen.AdaptiveFeatureGenerator;
import opennlp.tools.util.featuregen.AdditionalContextFeatureGenerator;
import opennlp.tools.util.featuregen.CachedFeatureGenerator;
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.BaseModel;
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+");
private static class NameFinderSequenceValidator implements
SequenceValidator {
public boolean validSequence(int i, String[] inputSequence,
String[] outcomesSequence, String outcome) {
// outcome is formatted like "cont" or "sometype-cont", so we
// can check if it ends with "cont".
if (outcome.endsWith(CONTINUE)) {
int li = outcomesSequence.length - 1;
if (li == -1) {
return false;
} else if (outcomesSequence[li].endsWith(OTHER)) {
return false;
} else if (outcomesSequence[li].endsWith(CONTINUE)) {
// if it is continue, we have to check if previous match was of the same type
String previousNameType = extractNameType(outcomesSequence[li]);
String nameType = extractNameType(outcome);
if( previousNameType != null || nameType != null ) {
if( nameType != null ) {
if( nameType.equals(previousNameType) ){
return true;
}
}
return false; // outcomes types are not equal
}
}
}
return true;
}
}
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 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) {
this.model = model.getNameFinderModel();
if (generator != null)
contextGenerator = new DefaultNameContextGenerator(generator);
else
contextGenerator = new DefaultNameContextGenerator(createFeatureGenerator());
contextGenerator.addFeatureGenerator(
new WindowFeatureGenerator(additionalContextFeatureGenerator, 8, 8));
beam = new BeamSearch(beamSize, contextGenerator, this.model,
new NameFinderSequenceValidator(), beamSize);
}
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(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)
});
}
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 c = bestSequence.getOutcomes();
contextGenerator.updateAdaptiveData(tokens, (String[]) c.toArray(new String[c.size()]));
int start = -1;
int end = -1;
List spans = new ArrayList(tokens.length);
for (int li = 0; li < c.size(); li++) {
String chunkTag = (String) 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
* 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();
}
/**
* Returns an array of probabilities for each of the specified spans which is the product
* 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 samples,
AdaptiveFeatureGenerator generator, final Map resources,
int iterations, int cutoff) throws IOException {
Map manifestInfoEntries = new HashMap();
ModelUtil.addCutoffAndIterations(manifestInfoEntries, cutoff, iterations);
AdaptiveFeatureGenerator featureGenerator;
if (generator != null)
featureGenerator = generator;
else
featureGenerator = createFeatureGenerator();
EventStream eventStream = new NameFinderEventStream(samples, type,
new DefaultNameContextGenerator(featureGenerator));
HashSumEventStream hses = new HashSumEventStream(eventStream);
AbstractModel nameFinderModel = GIS.trainModel(iterations, new TwoPassDataIndexer(hses, cutoff));
manifestInfoEntries.put(BaseModel.TRAINING_EVENTHASH_PROPERTY,
hses.calculateHashSum().toString(16));
return new TokenNameFinderModel(languageCode, nameFinderModel,
resources, manifestInfoEntries);
}
public static TokenNameFinderModel train(String languageCode, String type, ObjectStream samples,
final Map resources, int iterations, int cutoff) throws IOException {
return train(languageCode, type, samples, null, resources, iterations, cutoff);
}
public static TokenNameFinderModel train(String languageCode, String type, ObjectStream samples,
final Map resources) throws IOException {
return NameFinderME.train(languageCode, type, samples, resources, 100, 5);
}
@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
*/
private 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.
*
*
* 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
*/
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()]);
}
/**
* Trains a new named entity model on the specified training file using the specified encoding to read it in.
*
* @param args [-encoding encoding] training_file model_file
*
* @throws java.io.IOException
*/
@Deprecated
public static void main(String[] args) throws IOException {
// Encoding must be specified !!!
// -encoding code train.file model.file
if (args.length == 4) {
NameSampleDataStream sampleStream = new NameSampleDataStream(
new PlainTextByLineStream(new InputStreamReader(new FileInputStream(args[2]), args[1])));
TokenNameFinderModel model =
NameFinderME.train("x-unspecified", "default", sampleStream, new HashMap());
model.serialize(new FileOutputStream(args[4]));
}
else {
// TODO: Usage
}
}
}