smile.nlp.keyword.CooccurrenceKeywords Maven / Gradle / Ivy
The newest version!
/*
* Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
*
* Smile is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Smile is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Smile. If not, see
* - Stem words by Porter algorithm and extract phrases based APRIORI algorithm
* (upto 4 words with frequency more than 3 times). Discard stop words.
*
- Select the top frequent terms up to 30% of running terms.
*
- Clustering frequent terms. Two terms are in the same cluster if
* either their Jensen-Shannon divergence or mutual information is
* above the threshold (0.95 * log 2, and log 2, respectively).
*
- Calculate the expected co-occurrence probability
*
- Calculate the refined χ2 values that removes the maximal term.
*
- Output a given number of terms of largest refined χ2 values.
*
*
* @author Haifeng Li
*/
public interface CooccurrenceKeywords {
/**
* Returns the top 10 keywords.
* @param text A single document.
* @return The top 10 keywords.
*/
static NGram[] of(String text) {
return of(text, 10);
}
/**
* Returns a given number of top keywords.
* @param text A single document.
* @param maxNumKeywords the maximum number of keywords.
* @return The top keywords.
*/
static NGram[] of(String text, int maxNumKeywords) {
ArrayList sentences = new ArrayList<>();
SimpleTokenizer tokenizer = new SimpleTokenizer();
PorterStemmer stemmer = new PorterStemmer();
// Split text into sentences. Stem words by Porter algorithm.
int ntotal = 0;
for (String paragraph : SimpleParagraphSplitter.getInstance().split(text)) {
for (String s : SimpleSentenceSplitter.getInstance().split(paragraph)) {
String[] sentence = tokenizer.split(s);
for (int i = 0; i < sentence.length; i++) {
sentence[i] = stemmer.stripPluralParticiple(sentence[i]).toLowerCase();
}
sentences.add(sentence);
ntotal += sentence.length;
}
}
// Extract phrases by Apriori-like algorithm.
int maxNGramSize = 4;
ArrayList terms = new ArrayList<>();
for (NGram[] ngrams : NGram.of(sentences, maxNGramSize, 4)) {
Collections.addAll(terms, ngrams);
}
Collections.sort(terms);
// Select upto 30% most frequent terms.
int n = 3 * terms.size() / 10;
NGram[] freqTerms = new NGram[n];
for (int i = 0, start = terms.size() - n; i < n; i++) {
freqTerms[i] = terms.get(start + i);
}
// Trie for phrase matching.
Trie trie = new Trie<>();
for (int i = 0; i < n; i++) {
trie.put(freqTerms[i].words, i);
}
// Build co-occurrence table
int[] nw = new int[n];
int[][] table = new int[n][n];
for (String[] sentence : sentences) {
Set phrases = new HashSet<>();
for (int j = 1; j <= maxNGramSize; j++) {
for (int i = 0; i <= sentence.length - j; i++) {
String[] phrase = Arrays.copyOfRange(sentence, i, i+j);
Integer index = trie.get(phrase);
if (index != null) {
phrases.add(index);
}
}
}
for (int i : phrases) {
nw[i] += phrases.size();
for (int j : phrases) {
if (i != j) {
table[i][j]++;
}
}
}
}
// Clustering frequent terms.
int[] cluster = new int[n];
for (int i = 0; i < cluster.length; i++) {
cluster[i] = i;
}
//double log2 = Math.log(2.0);
for (int i = 0; i < n; i++) {
for (int j = i + 1; j < n; j++) {
// Mutual information
if (table[i][j] > 0) {
// This doesn't work as ntotal is usually large and thus the mutual information
// is way larger than the threshold log2 given in the paper.
//double mutual = Math.log((double) ntotal * table[i][j] / (freqTerms[i].freq * freqTerms[j].freq));
// Here we just use the (squared) geometric average of co-occurrence probability
// It works well to clustering things like "digital computer" and "computer" in practice.
double mutual = (double) table[i][j] * table[i][j] / (freqTerms[i].count * freqTerms[j].count);
if (mutual >= 0.25) {
cluster[j] = cluster[i];
} /*else {
double js = 0.0; // Jsensen-Shannon divergence
for (int k = 0; k < n; k++) {
double p1 = (double) table[i][k] / freqTerms[i].freq;
double p2 = (double) table[j][k] / freqTerms[j].freq;
// The formula in the paper is not correct as p is not real probablity.
if (p1 > 0 && p2 > 0) {
js += -(p1+p2) * Math.log((p1+p2)/2.0) + p1 * Math.log(p1) + p2 * Math.log(p2);
}
}
js /= 2.0;
if (js > log2) {
cluster[j] = cluster[i];
}
}*/
}
}
}
// Calculate expected probability
double[] pc = new double[n];
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
pc[cluster[j]] += table[i][j];
}
}
for (int i = 0; i < n; i++) {
pc[i] /= ntotal;
}
// Calculate chi-square scores.
double[] score = new double[n];
for (int i = 0; i < n; i++) {
double max = Double.NEGATIVE_INFINITY;
for (int j = 0; j < n; j++) {
if (cluster[j] != j) {
continue;
}
double fwc = 0.0;
for (int k = 0; k < n; k++) {
if (cluster[k] == j)
fwc += table[i][k];
}
double expected = nw[i] * pc[j];
double d = (fwc - expected);
double chisq = d * d / expected;
score[i] += chisq;
if (chisq > max) max = chisq;
}
//score[i] -= max;
}
int[] index = QuickSort.sort(score);
ArrayList keywords = new ArrayList<>();
for (int i = n; i-- > 0; ) {
boolean add = true;
// filter out components of phrases, e.g. "digital" in "digital computer".
for (int j = i+1; j < n; j++) {
if (cluster[index[j]] == cluster[index[i]]) {
if (freqTerms[index[j]].words.length >= freqTerms[index[i]].words.length) {
add = false;
break;
} else {
keywords.remove(freqTerms[index[j]]);
add = true;
}
}
}
if (add) {
keywords.add(freqTerms[index[i]]);
if (keywords.size() >= maxNumKeywords) break;
}
}
return keywords.toArray(new NGram[0]);
}
}