opennlp.tools.languagemodel.NGramLanguageModel.java Source code

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Here is the source code for opennlp.tools.languagemodel.NGramLanguageModel.java

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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 += Math.log(score);
                if (Double.isNaN(probability)) {
                    probability = 0d;
                    break;
                }
            }
            probability = Math.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 += Math.log(score);
                if (Double.isNaN(probability)) {
                    probability = 0d;
                    break;
                }
            }
            probability = Math.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));
        }

    }

}