Package org.terrier.matching.models
Provides the classes that implement various weighting models. Generally, the models fall into two classes:
 Term weighting models  score an occurrence of a document regardless of where the occurrences appear.
 Fieldbased weighting models  score an occurrence of a document depending on the fields that the term occurs in.
 IDF: e.g. TF_IDF (aka BM11), BM25, LemurTF_IDF.
 Divergence from Randomness: e.g. PL2, InL2. Arbitrary DFR models can also be generated using DFRWeightingModel
 hypergeometric Divergence from Randomness: e.g. DLH, DPH, DFRee
 Language modelling: e.g. Hiemstra_LM, Dirichlet_LM
 Divergence from Independence: DFI0
 BM25F
 PL2F
 MDL2
 ML2
 Arbitrary perfield normalisation models can be generated using the PerFieldNormWeightingModel model.

Class Summary Class Description 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 TREC2004].CoordinateMatch A weighting model that returns 1 for each match.DFIC Divergence From Independence model based on Chisquare statistics (i.e., standardized Chisquared distance from independence in term frequency tf).DFIZ Divergence From Independence model based on Standardization (i.e., standardized distance from independence in term frequency tf).DFR_BM25 This class implements the DFR_BM25 weighting model.DFRee This class implements the DFRee weighting model.DFReeKLIM This class implements the DFReeKLIM weighting model.DFRWeightingModel This class implements a modular Divergence from Randomness weighting model.DirichletLM Bayesian smoothing with Dirichlet Prior.Dl This class implements a simple document length weighting model.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 In_expB2 weighting model, namely Inverse Expected Document Frequency model with Bernoulli aftereffect and normalisation 2.In_expC2 This class implements the In_expC2 weighting model.InB2 This class implements the InB2 weighting model, namely Inverse Document Frequency model with Bernoulli aftereffect and normalisation 2.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 fieldbased weighting model.ML2 This class implements the ML2 fieldbased weighting model.Null A weighting model that returns 0 for each match.PerFieldNormWeightingModel A class for generating arbitrary perfield normalisation models.PL2 This class implements the PL2 weighting model.PL2F A convenience subclass of PerFieldNormWeightingModel setup to do specifically PL2F.SingleFieldModel Use a normal weighting model on a predetermine subset of the field.StaticFeature Class for query independent features loaded from file.StaticScoreModifierWeightingModel Base abstract class for query independent features loaded from file.Tf This class implements a simple Tf weighting model.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.WeightingModelLibrary A library of tf normalizations for weighting models such as the pivoted length normalization described in Singhal et al., 1996.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.