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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.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 k the smoothing factor
* @return the Laplace smoothing probability
* @see Additive Smoothing
*/
public static double calculateLaplaceSmoothingProbability(StringList ngram,
Iterable set, 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 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 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 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 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 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 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 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 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 flatSet(Iterable set) {
Collection 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 getNGrams(StringList sequence, int size) {
Collection 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;
}
}
