| /* |
| * 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.ngram; |
| |
| import java.util.Collection; |
| import java.util.HashSet; |
| import java.util.LinkedList; |
| |
| import opennlp.tools.util.StringList; |
| |
| /** |
| * Utility class for ngrams. |
| * Some methods apply specifically to certain 'n' values, for e.g. tri/bi/uni-grams. |
| */ |
| public class NGramUtils { |
| |
| /** |
| * calculate the probability of a ngram in a vocabulary using Laplace smoothing algorithm |
| * |
| * @param ngram the ngram to get the probability for |
| * @param set the vocabulary |
| * @param size the size of the vocabulary |
| * @param k the smoothing factor |
| * @return the Laplace smoothing probability |
| * @see <a href="https://en.wikipedia.org/wiki/Additive_smoothing">Additive Smoothing</a> |
| */ |
| public static double calculateLaplaceSmoothingProbability(StringList ngram, Iterable<StringList> set, int size, Double k) { |
| return (count(ngram, set) + k) / (count(getNMinusOneTokenFirst(ngram), set) + k * 1); |
| } |
| |
| /** |
| * calculate the probability of a unigram in a vocabulary using maximum likelihood estimation |
| * |
| * @param word the only word in the unigram |
| * @param set the vocabulary |
| * @return the maximum likelihood probability |
| */ |
| public static double calculateUnigramMLProbability(String word, Collection<StringList> set) { |
| double vocSize = 0d; |
| for (StringList s : set) { |
| vocSize += s.size(); |
| } |
| return count(new StringList(word), set) / vocSize; |
| } |
| |
| /** |
| * calculate the probability of a bigram in a vocabulary using maximum likelihood estimation |
| * |
| * @param x0 first word in the bigram |
| * @param x1 second word in the bigram |
| * @param set the vocabulary |
| * @return the maximum likelihood probability |
| */ |
| public static double calculateBigramMLProbability(String x0, String x1, Collection<StringList> set) { |
| return calculateNgramMLProbability(new StringList(x0, x1), set); |
| } |
| |
| /** |
| * calculate the probability of a trigram in a vocabulary using maximum likelihood estimation |
| * |
| * @param x0 first word in the trigram |
| * @param x1 second word in the trigram |
| * @param x2 third word in the trigram |
| * @param set the vocabulary |
| * @return the maximum likelihood probability |
| */ |
| public static double calculateTrigramMLProbability(String x0, String x1, String x2, Iterable<StringList> set) { |
| return calculateNgramMLProbability(new StringList(x0, x1, x2), set); |
| } |
| |
| /** |
| * calculate the probability of a ngram in a vocabulary using maximum likelihood estimation |
| * |
| * @param ngram a ngram |
| * @param set the vocabulary |
| * @return the maximum likelihood probability |
| */ |
| public static double calculateNgramMLProbability(StringList ngram, Iterable<StringList> set) { |
| StringList ngramMinusOne = getNMinusOneTokenFirst(ngram); |
| return count(ngram, set) / count(ngramMinusOne, set); |
| } |
| |
| /** |
| * calculate the probability of a bigram in a vocabulary using prior Laplace smoothing algorithm |
| * |
| * @param x0 the first word in the bigram |
| * @param x1 the second word in the bigram |
| * @param set the vocabulary |
| * @param k the smoothing factor |
| * @return the prior Laplace smoothiing probability |
| */ |
| public static double calculateBigramPriorSmoothingProbability(String x0, String x1, Collection<StringList> set, Double k) { |
| return (count(new StringList(x0, x1), set) + k * calculateUnigramMLProbability(x1, set)) / |
| (count(new StringList(x0), set) + k * set.size()); |
| } |
| |
| /** |
| * calculate the probability of a trigram in a vocabulary using a linear interpolation algorithm |
| * |
| * @param x0 the first word in the trigram |
| * @param x1 the second word in the trigram |
| * @param x2 the third word in the trigram |
| * @param set the vocabulary |
| * @param lambda1 trigram interpolation factor |
| * @param lambda2 bigram interpolation factor |
| * @param lambda3 unigram interpolation factor |
| * @return the linear interpolation probability |
| */ |
| public static double calculateTrigramLinearInterpolationProbability(String x0, String x1, String x2, Collection<StringList> set, |
| Double lambda1, Double lambda2, Double lambda3) { |
| assert lambda1 + lambda2 + lambda3 == 1 : "lambdas sum should be equals to 1"; |
| assert lambda1 > 0 && lambda2 > 0 && lambda3 > 0 : "lambdas should all be greater than 0"; |
| |
| return lambda1 * calculateTrigramMLProbability(x0, x1, x2, set) + |
| lambda2 * calculateBigramMLProbability(x1, x2, set) + |
| lambda3 * calculateUnigramMLProbability(x2, set); |
| |
| } |
| |
| /** |
| * calculate the probability of a ngram in a vocabulary using the missing probability mass algorithm |
| * |
| * @param ngram the ngram |
| * @param discount discount factor |
| * @param set the vocabulary |
| * @return the probability |
| */ |
| public static double calculateMissingNgramProbabilityMass(StringList ngram, Double discount, Iterable<StringList> set) { |
| Double missingMass = 0d; |
| Double countWord = count(ngram, set); |
| for (String word : flatSet(set)) { |
| missingMass += (count(getNPlusOneNgram(ngram, word), set) - discount) / countWord; |
| } |
| return 1 - missingMass; |
| } |
| |
| /** |
| * get the (n-1)th ngram of a given ngram, that is the same ngram except the last word in the ngram |
| * |
| * @param ngram a ngram |
| * @return a ngram |
| */ |
| public static StringList getNMinusOneTokenFirst(StringList ngram) { |
| String[] tokens = new String[ngram.size() - 1]; |
| for (int i = 0; i < ngram.size() - 1; i++) { |
| tokens[i] = ngram.getToken(i); |
| } |
| return tokens.length > 0 ? new StringList(tokens) : null; |
| } |
| |
| /** |
| * get the (n-1)th ngram of a given ngram, that is the same ngram except the first word in the ngram |
| * |
| * @param ngram a ngram |
| * @return a ngram |
| */ |
| public static StringList getNMinusOneTokenLast(StringList ngram) { |
| String[] tokens = new String[ngram.size() - 1]; |
| for (int i = 1; i < ngram.size(); i++) { |
| tokens[i - 1] = ngram.getToken(i); |
| } |
| return tokens.length > 0 ? new StringList(tokens) : null; |
| } |
| |
| private static StringList getNPlusOneNgram(StringList ngram, String word) { |
| String[] tokens = new String[ngram.size() + 1]; |
| for (int i = 0; i < ngram.size(); i++) { |
| tokens[i] = ngram.getToken(i); |
| } |
| tokens[tokens.length - 1] = word; |
| return new StringList(tokens); |
| } |
| |
| private static Double count(StringList ngram, Iterable<StringList> sentences) { |
| Double count = 0d; |
| for (StringList sentence : sentences) { |
| int idx0 = indexOf(sentence, ngram.getToken(0)); |
| if (idx0 >= 0 && sentence.size() >= idx0 + ngram.size()) { |
| boolean match = true; |
| for (int i = 1; i < ngram.size(); i++) { |
| String sentenceToken = sentence.getToken(idx0 + i); |
| String ngramToken = ngram.getToken(i); |
| match &= sentenceToken.equals(ngramToken); |
| } |
| if (match) { |
| count++; |
| } |
| } |
| } |
| return count; |
| } |
| |
| private static int indexOf(StringList sentence, String token) { |
| for (int i = 0; i < sentence.size(); i++) { |
| if (token.equals(sentence.getToken(i))) { |
| return i; |
| } |
| } |
| return -1; |
| } |
| |
| private static Collection<String> flatSet(Iterable<StringList> set) { |
| Collection<String> flatSet = new HashSet<>(); |
| for (StringList sentence : set) { |
| for (String word : sentence) { |
| flatSet.add(word); |
| } |
| } |
| return flatSet; |
| } |
| |
| /** |
| * get the ngrams of dimension n of a certain input sequence of tokens |
| * |
| * @param sequence a sequence of tokens |
| * @param size the size of the resulting ngrmams |
| * @return all the possible ngrams of the given size derivable from the input sequence |
| */ |
| public static Collection<StringList> getNGrams(StringList sequence, int size) { |
| Collection<StringList> ngrams = new LinkedList<>(); |
| if (size == -1 || size >= sequence.size()) { |
| ngrams.add(sequence); |
| } else { |
| String[] ngram = new String[size]; |
| for (int i = 0; i < sequence.size() - size + 1; i++) { |
| ngram[0] = sequence.getToken(i); |
| for (int j = 1; j < size; j++) { |
| ngram[j] = sequence.getToken(i + j); |
| } |
| ngrams.add(new StringList(ngram)); |
| } |
| } |
| |
| return ngrams; |
| } |
| |
| } |