Package org.apache.lucene.search.similarities
Similarity serves
as the base for ranking functions. For searching, users can employ the models
already implemented or create their own by extending one of the classes in this
package.
Table Of Contents
Summary of the Ranking Methods
BM25Similarity is an optimized
implementation of the successful Okapi BM25 model.
ClassicSimilarity is the original Lucene
scoring function. It is based on the
Vector Space Model. For more
information, see TFIDFSimilarity.
SimilarityBase provides a basic
implementation of the Similarity contract and exposes a highly simplified
interface, which makes it an ideal starting point for new ranking functions.
Lucene ships the following methods built on
SimilarityBase:
- Amati and Rijsbergen's DFR framework;
- Clinchant and Gaussier's Information-based models for IR;
- The implementation of two language models from Zhai and Lafferty's paper.
- Divergence from independence models as described in "IRRA at TREC 2012" (Dinçer).
SimilarityBase is not
optimized to the same extent as
ClassicSimilarity and
BM25Similarity, a difference in
performance is to be expected when using the methods listed above. However,
optimizations can always be implemented in subclasses; see
below.
Changing Similarity
Chances are the available Similarities are sufficient for all
your searching needs.
However, in some applications it may be necessary to customize your Similarity implementation. For instance, some
applications do not need to distinguish between shorter and longer documents
and could set BM25's b
parameter to 0.
To change Similarity, one must do so for both indexing and
searching, and the changes must happen before
either of these actions take place. Although in theory there is nothing stopping you from changing mid-stream, it
just isn't well-defined what is going to happen.
To make this change, implement your own Similarity (likely
you'll want to simply subclass SimilarityBase), and
then register the new class by calling
IndexWriterConfig.setSimilarity(Similarity)
before indexing and
IndexSearcher.setSimilarity(Similarity)
before searching.
Tuning BM25Similarity
BM25Similarity has
two parameters that may be tuned:
- k1, which calibrates term frequency saturation and must be
positive or null. A value of
0makes term frequency completely ignored, making documents scored only based on the value of the IDF of the matched terms. Higher values of k1 increase the impact of term frequency on the final score. Default value is1.2. - b, which controls how much document length should normalize
term frequency values and must be in
[0, 1]. A value of0disables length normalization completely. Default value is0.75.
Extending SimilarityBase
The easiest way to quickly implement a new ranking method is to extend
SimilarityBase, which provides
basic implementations for the low level . Subclasses are only required to
implement the SimilarityBase.score(BasicStats, double, double)
and SimilarityBase.toString()
methods.
Another option is to extend one of the frameworks
based on SimilarityBase. These
Similarities are implemented modularly, e.g.
DFRSimilarity delegates
computation of the three parts of its formula to the classes
BasicModel,
AfterEffect and
Normalization. Instead of
subclassing the Similarity, one can simply introduce a new basic model and tell
DFRSimilarity to use it.
-
ClassDescriptionThis class acts as the base class for the implementations of the first normalization of the informative content in the DFR framework.Model of the information gain based on the ratio of two Bernoulli processes.Model of the information gain based on Laplace's law of succession.Axiomatic approaches for IR.F1EXP is defined as Sum(tf(term_doc_freq)*ln(docLen)*IDF(term)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freqF1LOG is defined as Sum(tf(term_doc_freq)*ln(docLen)*IDF(term)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freqF2EXP is defined as Sum(tfln(term_doc_freq, docLen)*IDF(term)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freqF2EXP is defined as Sum(tfln(term_doc_freq, docLen)*IDF(term)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freqF3EXP is defined as Sum(tf(term_doc_freq)*IDF(term)-gamma(docLen, queryLen)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq gamma(docLen, queryLen) = (docLen-queryLen)*queryLen*s/avdl NOTE: the gamma function of this similarity creates negative scoresF3EXP is defined as Sum(tf(term_doc_freq)*IDF(term)-gamma(docLen, queryLen)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq gamma(docLen, queryLen) = (docLen-queryLen)*queryLen*s/avdl NOTE: the gamma function of this similarity creates negative scoresThis class acts as the base class for the specific basic model implementations in the DFR framework.Geometric as limiting form of the Bose-Einstein model.An approximation of the I(ne) model.The basic tf-idf model of randomness.Tf-idf model of randomness, based on a mixture of Poisson and inverse document frequency.Stores all statistics commonly used ranking methods.BM25 Similarity.Collection statistics for the BM25 model.Simple similarity that gives terms a score that is equal to their query boost.Expert: Historical scoring implementation.Implements the Divergence from Independence (DFI) model based on Chi-square statistics (i.e., standardized Chi-squared distance from independence in term frequency tf).Implements the divergence from randomness (DFR) framework introduced in Gianni Amati and Cornelis Joost Van Rijsbergen.The probabilistic distribution used to model term occurrence in information-based models.Log-logistic distribution.The smoothed power-law (SPL) distribution for the information-based framework that is described in the original paper.Provides a framework for the family of information-based models, as described in Stéphane Clinchant and Eric Gaussier.Computes the measure of divergence from independence for DFI scoring functions.Normalized chi-squared measure of distance from independenceSaturated measure of distance from independenceStandardized measure of distance from independenceThe lambda (λw) parameter in information-based models.Computes lambda as
docFreq+1 / numberOfDocuments+1.Computes lambda astotalTermFreq+1 / numberOfDocuments+1.Bayesian smoothing using Dirichlet priors.Language model based on the Jelinek-Mercer smoothing method.Abstract superclass for language modeling Similarities.A strategy for computing the collection language model.Modelsp(w|C)as the number of occurrences of the term in the collection, divided by the total number of tokens+ 1.Stores the collection distribution of the current term.Implements the CombSUM method for combining evidence from multiple similarity values described in: Joseph A.This class acts as the base class for the implementations of the term frequency normalization methods in the DFR framework.Implementation used when there is no normalization.Normalization model that assumes a uniform distribution of the term frequency.Normalization model in which the term frequency is inversely related to the length.Dirichlet Priors normalizationPareto-Zipf NormalizationProvides the ability to use a differentSimilarityfor different fields.Similarity defines the components of Lucene scoring.Stores the weight for a query across the indexed collection.A subclass ofSimilaritythat provides a simplified API for its descendants.Implementation ofSimilaritywith the Vector Space Model.