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
|