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
Class Summary Class Description BB2This class implements the BB2 weighting model. BM25This class implements the Okapi BM25 weighting model. BM25FA convenience subclass of PerFieldNormWeightingModel setup to do specifically BM25F, as described by [Zaragoza TREC-2004]. CoordinateMatchA weighting model that returns 1 for each match. DFICDivergence From Independence model based on Chi-square statistics (i.e., standardized Chi-squared distance from independence in term frequency tf). DFIZDivergence From Independence model based on Standardization (i.e., standardized distance from independence in term frequency tf). DFR_BM25This class implements the DFR_BM25 weighting model. DFReeThis class implements the DFRee weighting model. DFReeKLIMThis class implements the DFReeKLIM weighting model. DFRWeightingModelThis class implements a modular Divergence from Randomness weighting model. DirichletLMBayesian smoothing with Dirichlet Prior. DlThis class implements a simple document length weighting model. DLHThis class implements the DLH weighting model. DLH13This class implements the DLH13 weighting model. DPHThis class implements the DPH hypergeometric weighting model. Hiemstra_LMThis class implements the Hiemstra LM weighting model. IdfThis class computes the idf values for specific terms in the collection. IFB2This class implements the IFB2 weighting model. In_expB2This class implements the In_expB2 weighting model, namely Inverse Expected Document Frequency model with Bernoulli after-effect and normalisation 2. In_expC2This class implements the In_expC2 weighting model. InB2This class implements the InB2 weighting model, namely Inverse Document Frequency model with Bernoulli after-effect and normalisation 2. InL2This class implements the InL2 weighting model. Js_KLsThis class implements the Js_KLs weighting model, which is the product of two measures: the Jefrreys' divergence with the Kullback Leibler's divergence. LemurTF_IDFThis class implements the TF_IDF weighting model as it is implemented in Lemur. LGDThis class implements the LGD weighting model. MDL2This class implements the MDL2 field-based weighting model. ML2This class implements the ML2 field-based weighting model. NullA weighting model that returns 0 for each match. PerFieldNormWeightingModelA class for generating arbitrary per-field normalisation models. PL2This class implements the PL2 weighting model. PL2FA convenience subclass of PerFieldNormWeightingModel setup to do specifically PL2F. SingleFieldModelUse a normal weighting model on a pre-determine subset of the field. StaticFeatureClass for query independent features loaded from file. StaticScoreModifierWeightingModelBase abstract class for query independent features loaded from file. TfThis class implements a simple Tf weighting model. TF_IDFThis class implements the TF_IDF weighting model. WeightingModelThis class should be extended by the classes used for weighting terms and documents. WeightingModelFactoryA factory method for handling the initialisation of weighting models. WeightingModelLibraryA library of tf normalizations for weighting models such as the pivoted length normalization described in Singhal et al., 1996. XSqrA_MThis 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.