public class DFRSimilarity extends SimilarityBase
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:
BasicModelG
: Geometric approximation of BoseEinstein
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
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.
Note that basic models BE (Limiting form of BoseEinstein), P (Poisson approximation of the Binomial) and D (Divergence approximation of the Binomial) are not implemented because their formula couldn't be written in a way that makes scores nondecreasing with the normalized term frequency.
BasicModel
,
AfterEffect
,
Normalization
Similarity.SimScorer
Modifier and Type  Field and Description 

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.

discountOverlaps
Constructor and Description 

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

Modifier and Type  Method and Description 

protected Explanation 
explain(BasicStats stats,
Explanation freq,
double docLen)
Explains the score.

protected void 
explain(List<Explanation> subs,
BasicStats stats,
double freq,
double 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 double 
score(BasicStats stats,
double freq,
double 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.

computeNorm, fillBasicStats, getDiscountOverlaps, log2, newStats, scorer, setDiscountOverlaps
protected final BasicModel basicModel
protected final AfterEffect afterEffect
protected final Normalization normalization
public DFRSimilarity(BasicModel basicModel, AfterEffect afterEffect, Normalization normalization)
Note that null
values are not allowed:
if you want no normalization, instead pass
Normalization.NoNormalization
.
basicModel
 Basic model of information contentafterEffect
 First normalization of information gainnormalization
 Second (length) normalizationprotected double score(BasicStats stats, double freq, double 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(List<Explanation> subs, BasicStats stats, double freq, double 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
subs
 the list of details of the explanation to extendstats
 the corpus level statistics.freq
 the term frequency.docLen
 the document length.protected Explanation explain(BasicStats stats, Explanation freq, double docLen)
SimilarityBase
SimilarityBase.score(BasicStats, double, double)
method) and the explanation for the term frequency. Subclasses content with
this format may add additional details in
SimilarityBase.explain(List, BasicStats, double, double)
.explain
in class SimilarityBase
stats
 the corpus level statistics.freq
 the term frequency and its explanation.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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