other,45-3-A94-1011,bq inherently extensible to more sophisticated <term> annotation </term> , and does not require a <term> pre-tagged
other,8-8-A94-1011,bq statistical systems </term> can exploit <term> sophisticated representations of documents </term> , and lends some support to the use
other,18-7-A94-1011,bq particularly suitable for exploiting <term> linguistically sophisticated representations </term> , and show that a <term> statistically
other,32-3-A94-1011,bq notions of <term> noun group </term> , <term> verb group </term> , and so on which is inherently extensible
other,29-3-A94-1011,bq methods </term> to derive notions of <term> noun group </term> , <term> verb group </term> , and so
other,8-9-A94-1011,bq paper reports on work done for the <term> LRE project SmTA double check </term> , which is creating a <term> PC based
tech,24-2-A94-1011,bq </term> are to be successfully applied in <term> IR </term> . A novel method for adding <term>
other,36-7-A94-1011,bq improved performance for sophisticated <term> representations </term> . It therefore shows that <term> statistical
tech,26-8-A94-1011,bq sophisticated representations </term> for <term> document classification </term> . This paper reports on work done
other,26-9-A94-1011,bq based tool </term> to be used in the <term> technical abstracting industry </term> . This paper proposes a model using
other,20-5-A94-1011,bq from the <term> weighted sum </term> of a <term> word set representation </term> . We investigate how sets of individually
other,25-6-A94-1011,bq theory about these probably-correct <term> rules </term> . We then proceed to repeat results
tech,18-1-A94-1011,bq in performance within the standard <term> term weighting statistical assignment paradigm </term> ( Fagan 1987 ; Lewis , 1992bc ; Buckley
other,40-4-A94-1011,bq descriptors ( keywords ) </term> than single <term> words </term> are . This leads us to consider the
other,23-4-A94-1011,bq representation </term> of them is that <term> linguistically sophisticated units </term> are more frequently individually
tech,10-7-A94-1011,bq repeat results which show that standard <term> statistical models </term> are not particularly suitable for
tech,16-2-A94-1011,bq if the power of recently developed <term> NLP techniques </term> are to be successfully applied in
tech,4-8-A94-1011,bq representations </term> . It therefore shows that <term> statistical systems </term> can exploit <term> sophisticated representations
other,7-6-A94-1011,bq sets of individually high-precision <term> rules </term> can result in a <term> low precision
tech,3-1-A94-1011,bq translation </term> use . The use of <term> NLP techniques </term> for <term> document classification </term>
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