Package org.terrier.matching.models

Provides the classes that implement various weighting models.

See:
          Description

Class Summary
BB2 This class implements the BB2 weighting model.
BM25 This class implements the Okapi BM25 weighting model.
BM25F A convenience subclass of PerFieldNormWeightingModel setup to do specifically BM25F, as described by [Zaragoza TREC-2004].
DFI0 Implementation of the basic Divergence from Independence model.
DFR_BM25 This class implements the DFR_BM25 weighting model.
DFRee This class implements the DFRee weighting model.
DFRWeightingModel This class implements a modular Divergence from Randomness weighting model.
DirichletLM Bayesian smoothing with Dirichlet Prior.
DLH This class implements the DLH weighting model.
DLH13 This class implements the DLH13 weighting model.
DPH This class implements the DPH hypergeometric weighting model.
Hiemstra_LM This class implements the Hiemstra LM weighting model.
Idf This class computes the idf values for specific terms in the collection.
IFB2 This class implements the IFB2 weighting model.
In_expB2 This class implements the PL2 weighting model.
In_expC2 This class implements the In_expC2 weighting model.
InB2 This class implements the PL2 weighting model.
InL2 This class implements the InL2 weighting model.
Js_KLs This class implements the Js_KLs weighting model, which is the product of two measures: the Jefrreys' divergence with the Kullback Leibler's divergence.
LemurTF_IDF This class implements the TF_IDF weighting model as it is implemented in Lemur.
LGD This class implements the LGD weighting model.
MDL2 This class implements the MDL2 field-based weighting model.
ML2 This class implements the ML2 field-based weighting model.
PerFieldNormWeightingModel A class for generating arbitrary per-field normalisation models.
PL2 This class implements the PL2 weighting model.
PL2F A convenience subclass of PerFieldNormWeightingModel setup to do specifically PL2F.
TF_IDF This class implements the TF_IDF weighting model.
WeightingModel This class should be extended by the classes used for weighting terms and documents.
WeightingModelFactory A factory method for handling the initialisation of weighting models.
XSqrA_M This class implements the XSqrA_M weighting model, which computed the inner product of Pearson's X^2 with the information growth computed with the multinomial M.
 

Package org.terrier.matching.models Description

Provides the classes that implement various weighting models. Generally, the models fall into two classes:

Various different families of weighting models are implemented: Of the field-based models, the following are implemented:



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