/*
* 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 org.apache.lucene.search.similarities;
import java.util.List;
import java.util.Locale;
import org.apache.lucene.search.Explanation;
/**
* Language model based on the Jelinek-Mercer smoothing method. From Chengxiang
* Zhai and John Lafferty. 2001. A study of smoothing methods for language
* models applied to Ad Hoc information retrieval. In Proceedings of the 24th
* annual international ACM SIGIR conference on Research and development in
* information retrieval (SIGIR '01). ACM, New York, NY, USA, 334-342.
* <p>The model has a single parameter, λ. According to said paper, the
* optimal value depends on both the collection and the query. The optimal value
* is around {@code 0.1} for title queries and {@code 0.7} for long queries.</p>
*
* @lucene.experimental
*/
public class LMJelinekMercerSimilarity extends LMSimilarity {
/** The λ parameter. */
private final float lambda;
/** Instantiates with the specified collectionModel and λ parameter. */
public LMJelinekMercerSimilarity(
CollectionModel collectionModel, float lambda) {
super(collectionModel);
this.lambda = lambda;
}
/** Instantiates with the specified λ parameter. */
public LMJelinekMercerSimilarity(float lambda) {
this.lambda = lambda;
}
@Override
protected float score(BasicStats stats, float freq, float docLen) {
return stats.getBoost() *
(float)Math.log(1 +
((1 - lambda) * freq / docLen) /
(lambda * ((LMStats)stats).getCollectionProbability()));
}
@Override
protected void explain(List<Explanation> subs, BasicStats stats, int doc,
float freq, float docLen) {
if (stats.getBoost() != 1.0f) {
subs.add(Explanation.match(stats.getBoost(), "boost"));
}
subs.add(Explanation.match(lambda, "lambda"));
super.explain(subs, stats, doc, freq, docLen);
}
/** Returns the λ parameter. */
public float getLambda() {
return lambda;
}
@Override
public String getName() {
return String.format(Locale.ROOT, "Jelinek-Mercer(%f)", getLambda());
}
}