Concordance

Query A94-1011 36 >
Sort Node 36 (1,678.4 per million)

other,36-7-A94-1011,bq	improved performance for sophisticated <term>	representations	</term> . It therefore shows that <term> statistical	#20132 We then proceed to repeat results which show that standard statistical models are not particularly suitable for exploiting linguistically sophisticated representations, and show that a statistically fitted rule-based model provides significantly improved performance for sophisticatedrepresentations.
other,7-6-A94-1011,bq	sets of individually high-precision <term>	rules	</term> can result in a <term> low precision	#20076 We investigate how sets of individually high-precisionrules can result in a low precision when used together, and develop some theory about these probably-correct rules.
other,25-6-A94-1011,bq	theory about these probably-correct <term>	rules	</term> . We then proceed to repeat results	#20094 We investigate how sets of individually high-precision rules can result in a low precision when used together, and develop some theory about these probably-correctrules.
other,8-8-A94-1011,bq	statistical systems </term> can exploit <term>	sophisticated representations of documents	</term> , and lends some support to the use	#20142 It therefore shows that statistical systems can exploitsophisticated representations of documents, and lends some support to the use of more linguistically sophisticated representations for document classification.
tech,10-7-A94-1011,bq	repeat results which show that standard <term>	statistical models	</term> are not particularly suitable for	#20106 We then proceed to repeat results which show that standardstatistical models are not particularly suitable for exploiting linguistically sophisticated representations, and show that a statistically fitted rule-based model provides significantly improved performance for sophisticated representations.
tech,15-3-A94-1011,bq	is presented which involves using a <term>	statistical POS tagger	</term> in conjunction with <term> unsupervised	#19960 A novel method for adding linguistic annotation to corpora is presented which involves using astatistical POS tagger in conjunction with unsupervised structure finding methods to derive notions of noun group, verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit.
tech,4-8-A94-1011,bq	representations </term> . It therefore shows that <term>	statistical systems	</term> can exploit <term> sophisticated representations	#20138 It therefore shows thatstatistical systems can exploit sophisticated representations of documents, and lends some support to the use of more linguistically sophisticated representations for document classification.
tech,26-7-A94-1011,bq	representations </term> , and show that a <term>	statistically fitted rule-based model	</term> provides significantly improved performance	#20122 We then proceed to repeat results which show that standard statistical models are not particularly suitable for exploiting linguistically sophisticated representations, and show that astatistically fitted rule-based model provides significantly improved performance for sophisticated representations.
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	#20189 This paper reports on work done for the LRE project SmTA double check, which is creating a PC based tool to be used in thetechnical abstracting industry.
tech,18-1-A94-1011,bq	in performance within the standard <term>	term weighting statistical assignment paradigm	</term> ( Fagan 1987 ; Lewis , 1992bc ; Buckley	#19901 The use of NLP techniques for document classification has not produced significant improvements in performance within the standardterm weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992bc; Buckley, 1993).
tech,21-3-A94-1011,bq	POS tagger </term> in conjunction with <term>	unsupervised structure finding methods	</term> to derive notions of <term> noun group	#19966 A novel method for adding linguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction withunsupervised structure finding methods to derive notions of noun group, verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit.
other,32-3-A94-1011,bq	notions of <term> noun group </term> , <term>	verb group	</term> , and so on which is inherently extensible	#19977 A novel method for adding linguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction with unsupervised structure finding methods to derive notions of noun group,verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit.
other,16-5-A94-1011,bq	phrases </term> rather than from the <term>	weighted sum	</term> of a <term> word set representation	#20061 This leads us to consider the assignment of descriptors from individual phrases rather than from theweighted sum of a word set representation.
other,15-4-A94-1011,bq	representation of documents </term> over a <term>	word set based representation	</term> of them is that <term> linguistically	#20017 One of the distinguishing features of a more linguistically sophisticated representation of documents over aword set based representation of them is that linguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords) than single words are.
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	#20065 This leads us to consider the assignment of descriptors from individual phrases rather than from the weighted sum of aword set representation.
other,40-4-A94-1011,bq	descriptors ( keywords ) </term> than single <term>	words	</term> are . This leads us to consider the	#20042 One of the distinguishing features of a more linguistically sophisticated representation of documents over a word set based representation of them is that linguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords) than singlewords are.


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