opennlp.tools.languagemodel.NGramLanguageModel 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.languagemodel;
import java.io.IOException;
import java.io.InputStream;
import opennlp.tools.ngram.NGramModel;
import opennlp.tools.ngram.NGramUtils;
import opennlp.tools.util.StringList;
/**
* A {@link opennlp.tools.languagemodel.LanguageModel} based on a {@link opennlp.tools.ngram.NGramModel}
* using Stupid Backoff to get the probabilities of the ngrams.
*/
public class NGramLanguageModel extends NGramModel implements LanguageModel {
private static final int DEFAULT_N = 3;
private final int n;
public NGramLanguageModel() {
this(DEFAULT_N);
}
public NGramLanguageModel(int n) {
this.n = n;
}
public NGramLanguageModel(InputStream in) throws IOException {
this(in, DEFAULT_N);
}
public NGramLanguageModel(InputStream in, int n)
throws IOException {
super(in);
this.n = n;
}
public void add(String... tokens) {
add(new StringList(tokens), 1, n);
}
@Override
public double calculateProbability(StringList tokens) {
double probability = 0d;
if (size() > 0) {
for (StringList ngram : NGramUtils.getNGrams(tokens, n)) {
double score = stupidBackoff(ngram);
probability += StrictMath.log(score);
if (Double.isNaN(probability)) {
probability = 0d;
break;
}
}
probability = StrictMath.exp(probability);
}
return probability;
}
@Override
public double calculateProbability(String... tokens) {
double probability = 0d;
if (size() > 0) {
for (String[] ngram : NGramUtils.getNGrams(tokens, n)) {
double score = stupidBackoff(new StringList(ngram));
probability += StrictMath.log(score);
if (Double.isNaN(probability)) {
probability = 0d;
break;
}
}
probability = StrictMath.exp(probability);
}
return probability;
}
@Override
public StringList predictNextTokens(StringList tokens) {
double maxProb = Double.NEGATIVE_INFINITY;
StringList token = null;
for (StringList ngram : this) {
String[] sequence = new String[ngram.size() + tokens.size()];
for (int i = 0; i < tokens.size(); i++) {
sequence[i] = tokens.getToken(i);
}
for (int i = 0; i < ngram.size(); i++) {
sequence[i + tokens.size()] = ngram.getToken(i);
}
StringList sample = new StringList(sequence);
double v = calculateProbability(sample);
if (v > maxProb) {
maxProb = v;
token = ngram;
}
}
return token;
}
@Override
public String[] predictNextTokens(String... tokens) {
double maxProb = Double.NEGATIVE_INFINITY;
String[] token = null;
for (StringList ngram : this) {
String[] sequence = new String[ngram.size() + tokens.length];
for (int i = 0; i < tokens.length; i++) {
sequence[i] = tokens[i];
}
for (int i = 0; i < ngram.size(); i++) {
sequence[i + tokens.length] = ngram.getToken(i);
}
double v = calculateProbability(sequence);
if (v > maxProb) {
maxProb = v;
token = new String[ngram.size()];
for (int i = 0; i < ngram.size(); i++) {
token[i] = ngram.getToken(i);
}
}
}
return token;
}
private double stupidBackoff(StringList ngram) {
int count = getCount(ngram);
StringList nMinusOneToken = NGramUtils.getNMinusOneTokenFirst(ngram);
if (nMinusOneToken == null || nMinusOneToken.size() == 0) {
return (double) count / (double) size();
} else if (count > 0) {
double countM1 = getCount(nMinusOneToken);
if (countM1 == 0d) {
countM1 = size(); // to avoid Infinite if n-1grams do not exist
}
return (double) count / countM1;
} else {
return 0.4 * stupidBackoff(NGramUtils.getNMinusOneTokenLast(ngram));
}
}
}