tech,45-3-A94-1011,ak inherently extensible to more sophisticated <term> annotation </term> , and does not require a <term> pre-tagged
other,11-8-A94-1011,ak sophisticated representations </term> of <term> documents </term> , and lends some support to the use
other,18-7-A94-1011,ak particularly suitable for exploiting <term> linguistically sophisticated representations </term> , and show that a <term> statistically
other,32-3-A94-1011,ak notions of <term> noun group </term> , <term> verb group </term> , and so on which is inherently extensible
other,29-3-A94-1011,ak methods </term> to derive notions of <term> noun group </term> , <term> verb group </term> , and so
tech,24-2-A94-1011,ak </term> are to be successfully applied in <term> IR </term> . A novel method for adding <term>
other,35-7-A94-1011,ak significantly improved performance for <term> sophisticated representations </term> . It therefore shows that <term> statistical
tech,26-8-A94-1011,ak sophisticated representations </term> for <term> document classification </term> . This paper reports on work done
other,20-5-A94-1011,ak from the <term> weighted sum </term> of a <term> word set representation </term> . We investigate how sets of individually
model,25-6-A94-1011,ak theory about these probably-correct <term> rules </term> . We then proceed to repeat results
tech,18-1-A94-1011,ak in performance within the standard <term> term weighting statistical assignment paradigm </term> ( Fagan 1987 ; Lewis , 1992bc ; Buckley
other,40-4-A94-1011,ak descriptors ( keywords ) </term> than single <term> words </term> are . This leads us to consider the
model,10-7-A94-1011,ak repeat results which show that standard <term> statistical models </term> are not particularly suitable for
tech,16-2-A94-1011,ak if the power of recently developed <term> NLP techniques </term> are to be successfully applied in
tech,4-8-A94-1011,ak representations </term> . It therefore shows that <term> statistical systems </term> can exploit <term> sophisticated representations
model,6-6-A94-1011,ak investigate how sets of individually <term> high-precision rules </term> can result in a low <term> precision
tech,3-1-A94-1011,ak practical translation use . The use of <term> NLP techniques </term> for <term> document classification </term>
other,22-8-A94-1011,ak lends some support to the use of more <term> linguistically sophisticated representations </term> for <term> document classification </term>
other,8-5-A94-1011,ak leads us to consider the assignment of <term> descriptors </term> from individual <term> phrases </term>
tech,6-1-A94-1011,ak use of <term> NLP techniques </term> for <term> document classification </term> has not produced significant improvements
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