See: Description
| 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].
|
| 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.
|
| 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.
|
Provides the classes that implement various weighting models. Generally, the models fall into two classes:
Terrier Information Retrieval Platform4.1. Copyright © 2004-2015, University of Glasgow