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Description
Class Summary | |
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Normalisation | This class provides a contract for implementing frequency normalisation methods. |
Normalisation0 | This class implements an empty normalisation. |
Normalisation1 | This class implements the DFR normalisation 1, which is identical to DFR normalisation 2 with c=1.0 |
Normalisation2 | This class implements the DFR normalisation 2. |
Normalisation2exp | This class implements the DFR normalisation 2 with natural logorithm. |
Normalisation3 | This class implements the Dirichlet Priors normalisation. |
NormalisationB | This class implements BM25's normalisation method. |
NormalisationF | This class implements an increasing density function for the frequency normalisation. |
NormalisationJ | This class implements the tf normalisation based on Jelinek-Mercer smoothing for language modelling. |
NormalisationJN | This class implements the tf normalisation based on Jelinek-Mercer smoothing for language modelling where collection model is given by document frequency instead of term frequency. |
NormalisationP | This class implements Term Frequency Normalisation via Pareto Distributions |
NormalisationStatic | This class implements a Normalisation method that forces all term frequencies to the value of the parameter. |
Provides the interface and the classes for implementing the basic models for randomness in the DFR framework. The implemented classes can be used by specifying the class name in /etc/trec.models, or in property trec.model. For instance, adding a line DFRWeightingModel(P,L,2) in file /etc/trec.models tells the systems to use the PL2 model to run retrieval. P stands for class P in this package, L stands for class L in package org.terrier.matching.basicmodel, and "2" stands for class 2 in package org.terrier.matching.models.normalisation.
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