org.elasticsearch.search.suggest.phrase.NoisyChannelSpellChecker Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of elasticsearch Show documentation
Show all versions of elasticsearch Show documentation
Elasticsearch subproject :server
/*
* Licensed to Elasticsearch under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch 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 org.elasticsearch.search.suggest.phrase;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.shingle.ShingleFilter;
import org.apache.lucene.analysis.synonym.SynonymFilter;
import org.apache.lucene.analysis.tokenattributes.TypeAttribute;
import org.apache.lucene.codecs.TermStats;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.BytesRefBuilder;
import org.elasticsearch.search.suggest.phrase.DirectCandidateGenerator.Candidate;
import org.elasticsearch.search.suggest.phrase.DirectCandidateGenerator.CandidateSet;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
final class NoisyChannelSpellChecker {
public static final double REAL_WORD_LIKELIHOOD = 0.95d;
public static final int DEFAULT_TOKEN_LIMIT = 10;
private final double realWordLikelihood;
private final boolean requireUnigram;
private final int tokenLimit;
NoisyChannelSpellChecker(double nonErrorLikelihood, boolean requireUnigram, int tokenLimit) {
this.realWordLikelihood = nonErrorLikelihood;
this.requireUnigram = requireUnigram;
this.tokenLimit = tokenLimit;
}
Result getCorrections(TokenStream stream, final CandidateGenerator generator,
float maxErrors, int numCorrections, WordScorer wordScorer, float confidence, int gramSize) throws IOException {
final List candidateSetsList = new ArrayList<>();
DirectCandidateGenerator.analyze(stream, new DirectCandidateGenerator.TokenConsumer() {
CandidateSet currentSet = null;
private TypeAttribute typeAttribute;
private final BytesRefBuilder termsRef = new BytesRefBuilder();
private boolean anyUnigram = false;
private boolean anyTokens = false;
@Override
public void reset(TokenStream stream) {
super.reset(stream);
typeAttribute = stream.addAttribute(TypeAttribute.class);
}
@Override
public void nextToken() throws IOException {
anyTokens = true;
BytesRef term = fillBytesRef(termsRef);
if (requireUnigram && typeAttribute.type() == ShingleFilter.DEFAULT_TOKEN_TYPE) {
return;
}
anyUnigram = true;
if (posIncAttr.getPositionIncrement() == 0 && typeAttribute.type() == SynonymFilter.TYPE_SYNONYM) {
assert currentSet != null;
TermStats termStats = generator.termStats(term);
if (termStats.docFreq > 0) {
currentSet.addOneCandidate(generator.createCandidate(BytesRef.deepCopyOf(term), termStats, realWordLikelihood));
}
} else {
if (currentSet != null) {
candidateSetsList.add(currentSet);
}
currentSet = new CandidateSet(Candidate.EMPTY, generator.createCandidate(BytesRef.deepCopyOf(term), true));
}
}
@Override
public void end() {
if (currentSet != null) {
candidateSetsList.add(currentSet);
}
if (requireUnigram && !anyUnigram && anyTokens) {
throw new IllegalStateException("At least one unigram is required but all tokens were ngrams");
}
}
});
if (candidateSetsList.isEmpty() || candidateSetsList.size() >= tokenLimit) {
return Result.EMPTY;
}
for (CandidateSet candidateSet : candidateSetsList) {
generator.drawCandidates(candidateSet);
}
double cutoffScore = Double.MIN_VALUE;
CandidateScorer scorer = new CandidateScorer(wordScorer, numCorrections, gramSize);
CandidateSet[] candidateSets = candidateSetsList.toArray(new CandidateSet[candidateSetsList.size()]);
if (confidence > 0.0) {
Candidate[] candidates = new Candidate[candidateSets.length];
for (int i = 0; i < candidates.length; i++) {
candidates[i] = candidateSets[i].originalTerm;
}
double inputPhraseScore = scorer.score(candidates, candidateSets);
cutoffScore = inputPhraseScore * confidence;
}
Correction[] bestCandidates = scorer.findBestCandiates(candidateSets, maxErrors, cutoffScore);
return new Result(bestCandidates, cutoffScore);
}
static class Result {
public static final Result EMPTY = new Result(Correction.EMPTY, Double.MIN_VALUE);
public final Correction[] corrections;
public final double cutoffScore;
private Result(Correction[] corrections, double cutoffScore) {
this.corrections = corrections;
this.cutoffScore = cutoffScore;
}
}
}