public class DFRSimilarity extends SimilarityBaseImplements 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), 357-389.
The DFR scoring formula is composed of three separate components: the basic model, the aftereffect and an additional normalization component, represented by the classes
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 Bose-Einstein
BasicModelG: Geometric approximation of Bose-Einstein
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:
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 term-occurrence in the query, is not handled by this implementation.
All Methods Instance Methods Concrete Methods Modifier and Type Method Description
getAfterEffect()Returns the first normalization
getBasicModel()Returns the basic model of information content
getNormalization()Returns the second normalization
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, getDiscountOverlaps, log2, setDiscountOverlaps, simScorer
public DFRSimilarity(BasicModel basicModel, AfterEffect afterEffect, Normalization normalization)Creates DFRSimilarity from the three components.
basicModel- Basic model of information content
afterEffect- First normalization of information gain
normalization- Second (length) normalization
public java.lang.String toString()Description copied from class:
public BasicModel getBasicModel()Returns the basic model of information content
public AfterEffect getAfterEffect()Returns the first normalization
public Normalization getNormalization()Returns the second normalization