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java.lang.Object org.apache.lucene.search.similarities.Similarity org.apache.lucene.search.similarities.SimilarityBase org.apache.lucene.search.similarities.DFRSimilarity
public class DFRSimilarity
Implements the divergence from randomness (DFR) framework introduced in Gianni Amati and Cornelis Joost Van Rijsbergen. 2002. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20, 4 (October 2002), 357389.
The DFR scoring formula is composed of three separate components: the
basic model, the aftereffect and an additional
normalization component, represented by the classes
BasicModel
, AfterEffect
and Normalization
,
respectively. The names of these classes were chosen to match the names of
their counterparts in the Terrier IR engine.
To construct a DFRSimilarity, you must specify the implementations for all three components of DFR:
BasicModel
: Basic model of information content:
BasicModelBE
: Limiting form of BoseEinstein
BasicModelG
: Geometric approximation of BoseEinstein
BasicModelP
: Poisson approximation of the Binomial
BasicModelD
: Divergence approximation of the Binomial
BasicModelIn
: Inverse document frequency
BasicModelIne
: Inverse expected document
frequency [mixture of Poisson and IDF]
BasicModelIF
: Inverse term frequency
[approximation of I(ne)]
AfterEffect
: First normalization of information
gain:
AfterEffectL
: Laplace's law of succession
AfterEffectB
: Ratio of two Bernoulli processes
AfterEffect.NoAfterEffect
: no first normalization
Normalization
: Second (length) normalization:
NormalizationH1
: Uniform distribution of term
frequency
NormalizationH2
: term frequency density inversely
related to length
NormalizationH3
: term frequency normalization
provided by Dirichlet prior
NormalizationZ
: term frequency normalization provided
by a Zipfian relation
Normalization.NoNormalization
: no second normalization
Note that qtf, the multiplicity of termoccurrence in the query, is not handled by this implementation.
BasicModel
,
AfterEffect
,
Normalization
Nested Class Summary 

Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity 

Similarity.SimScorer, Similarity.SimWeight 
Field Summary  

protected AfterEffect 
afterEffect
The first normalization of the information content. 
protected BasicModel 
basicModel
The basic model for information content. 
protected Normalization 
normalization
The term frequency normalization. 
Fields inherited from class org.apache.lucene.search.similarities.SimilarityBase 

discountOverlaps 
Constructor Summary  

DFRSimilarity(BasicModel basicModel,
AfterEffect afterEffect,
Normalization normalization)
Creates DFRSimilarity from the three components. 
Method Summary  

protected void 
explain(Explanation expl,
BasicStats stats,
int doc,
float freq,
float docLen)
Subclasses should implement this method to explain the score. 
AfterEffect 
getAfterEffect()
Returns the first normalization 
BasicModel 
getBasicModel()
Returns the basic model of information content 
Normalization 
getNormalization()
Returns the second normalization 
protected float 
score(BasicStats stats,
float freq,
float docLen)
Scores the document doc . 
String 
toString()
Subclasses must override this method to return the name of the Similarity and preferably the values of parameters (if any) as well. 
Methods inherited from class org.apache.lucene.search.similarities.SimilarityBase 

computeNorm, computeWeight, decodeNormValue, encodeNormValue, explain, fillBasicStats, getDiscountOverlaps, log2, newStats, setDiscountOverlaps, simScorer 
Methods inherited from class org.apache.lucene.search.similarities.Similarity 

coord, queryNorm 
Methods inherited from class java.lang.Object 

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait 
Field Detail 

protected final BasicModel basicModel
protected final AfterEffect afterEffect
protected final Normalization normalization
Constructor Detail 

public DFRSimilarity(BasicModel basicModel, AfterEffect afterEffect, Normalization normalization)
Note that null
values are not allowed:
if you want no normalization or aftereffect, instead pass
Normalization.NoNormalization
or AfterEffect.NoAfterEffect
respectively.
basicModel
 Basic model of information contentafterEffect
 First normalization of information gainnormalization
 Second (length) normalizationMethod Detail 

protected float score(BasicStats stats, float freq, float docLen)
SimilarityBase
doc
.
Subclasses must apply their scoring formula in this class.
score
in class SimilarityBase
stats
 the corpus level statistics.freq
 the term frequency.docLen
 the document length.
protected void explain(Explanation expl, BasicStats stats, int doc, float freq, float docLen)
SimilarityBase
expl
already contains the score, the name of the class and the doc id, as well
as the term frequency and its explanation; subclasses can add additional
clauses to explain details of their scoring formulae.
The default implementation does nothing.
explain
in class SimilarityBase
expl
 the explanation to extend with details.stats
 the corpus level statistics.doc
 the document id.freq
 the term frequency.docLen
 the document length.public String toString()
SimilarityBase
toString
in class SimilarityBase
public BasicModel getBasicModel()
public AfterEffect getAfterEffect()
public Normalization getNormalization()


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