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.
- Field-based 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
- hyper-geometric 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 per-field normalisation models can be generated using the PerFieldNormWeightingModel model.
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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 TREC-2004].CoordinateMatch A weighting model that returns 1 for each match.DFIC Divergence From Independence model based on Chi-square statistics (i.e., standardized Chi-squared 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 after-effect 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 after-effect 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 field-based weighting model.ML2 This class implements the ML2 field-based weighting model.Null A weighting model that returns 0 for each match.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.SingleFieldModel Use a normal weighting model on a pre-determine 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.