class |
Axiomatic |
Axiomatic approaches for IR.
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class |
AxiomaticF1EXP |
F1EXP is defined as Sum(tf(term_doc_freq)*ln(docLen)*IDF(term))
where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq
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class |
AxiomaticF1LOG |
F1LOG is defined as Sum(tf(term_doc_freq)*ln(docLen)*IDF(term))
where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq
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class |
AxiomaticF2EXP |
F2EXP is defined as Sum(tfln(term_doc_freq, docLen)*IDF(term))
where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq
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class |
AxiomaticF2LOG |
F2EXP is defined as Sum(tfln(term_doc_freq, docLen)*IDF(term))
where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq
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class |
AxiomaticF3EXP |
F3EXP is defined as Sum(tf(term_doc_freq)*IDF(term)-gamma(docLen, queryLen))
where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq
gamma(docLen, queryLen) = (docLen-queryLen)*queryLen*s/avdl
NOTE: the gamma function of this similarity creates negative scores
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class |
AxiomaticF3LOG |
F3EXP is defined as Sum(tf(term_doc_freq)*IDF(term)-gamma(docLen, queryLen))
where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq
gamma(docLen, queryLen) = (docLen-queryLen)*queryLen*s/avdl
NOTE: the gamma function of this similarity creates negative scores
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class |
DFISimilarity |
Implements the Divergence from Independence (DFI) model based on Chi-square statistics
(i.e., standardized Chi-squared distance from independence in term frequency tf).
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class |
DFRSimilarity |
Implements the divergence from randomness (DFR) framework
introduced in Gianni Amati and Cornelis Joost Van Rijsbergen.
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class |
IBSimilarity |
Provides a framework for the family of information-based models, as described
in Stéphane Clinchant and Eric Gaussier.
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class |
LMDirichletSimilarity |
Bayesian smoothing using Dirichlet priors.
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class |
LMJelinekMercerSimilarity |
Language model based on the Jelinek-Mercer smoothing method.
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class |
LMSimilarity |
Abstract superclass for language modeling Similarities.
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