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/*
* Licensed to ElasticSearch and Shay Banon under one
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* 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
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* Unless required by applicable law or agreed to in writing,
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package org.elasticsearch.search.suggest.phrase;
import java.io.IOException;
import java.util.List;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.search.spell.DirectSpellChecker;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.CharsRef;
import org.apache.lucene.util.UnicodeUtil;
import org.elasticsearch.common.text.StringText;
import org.elasticsearch.common.text.Text;
import org.elasticsearch.search.suggest.Suggest.Suggestion;
import org.elasticsearch.search.suggest.Suggest.Suggestion.Entry;
import org.elasticsearch.search.suggest.Suggest.Suggestion.Entry.Option;
import org.elasticsearch.search.suggest.SuggestContextParser;
import org.elasticsearch.search.suggest.SuggestUtils;
import org.elasticsearch.search.suggest.Suggester;
public final class PhraseSuggester implements Suggester {
private final BytesRef SEPARATOR = new BytesRef(" ");
/*
* More Ideas:
* - add ability to find whitespace problems -> we can build a poor mans decompounder with our index based on a automaton?
* - add ability to build different error models maybe based on a confusion matrix?
* - try to combine a token with its subsequent token to find / detect word splits (optional)
* - for this to work we need some way to defined the position length of a candidate
* - phonetic filters could be interesting here too for candidate selection
*/
@Override
public Suggestion> execute(String name, PhraseSuggestionContext suggestion,
IndexReader indexReader, CharsRef spare) throws IOException {
double realWordErrorLikelihood = suggestion.realworldErrorLikelyhood();
List generators = suggestion.generators();
CandidateGenerator[] gens = new CandidateGenerator[generators.size()];
for (int i = 0; i < gens.length; i++) {
PhraseSuggestionContext.DirectCandidateGenerator generator = generators.get(i);
DirectSpellChecker directSpellChecker = SuggestUtils.getDirectSpellChecker(generator);
gens[i] = new DirectCandidateGenerator(directSpellChecker, generator.field(), generator.suggestMode(), indexReader, realWordErrorLikelihood, generator.size(), generator.preFilter(), generator.postFilter());
}
final NoisyChannelSpellChecker checker = new NoisyChannelSpellChecker(realWordErrorLikelihood, suggestion.getRequireUnigram(), suggestion.getTokenLimit());
final BytesRef separator = suggestion.separator();
TokenStream stream = checker.tokenStream(suggestion.getAnalyzer(), suggestion.getText(), spare, suggestion.getField());
WordScorer wordScorer = suggestion.model().newScorer(indexReader, suggestion.getField(), realWordErrorLikelihood, separator);
Correction[] corrections = checker.getCorrections(stream, new MultiCandidateGeneratorWrapper(suggestion.getShardSize(), gens), suggestion.maxErrors(),
suggestion.getShardSize(), indexReader,wordScorer , separator, suggestion.confidence(), suggestion.gramSize());
UnicodeUtil.UTF8toUTF16(suggestion.getText(), spare);
Suggestion.Entry
