Concordance

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

other,16-5-A94-1011,bq	This leads us to consider the assignment of <term> descriptors </term> from individual <term> phrases </term> rather than from the <term> weighted sum </term> of a <term> word set representation </term> .	#20061 This leads us to consider the assignment of descriptors from individual phrases rather than from theweighted sum of a word set representation.
other,20-5-A94-1011,bq	This leads us to consider the assignment of <term> descriptors </term> from individual <term> phrases </term> rather than from the <term> weighted sum </term> of a <term> word set representation </term> .	#20065 This leads us to consider the assignment of descriptors from individual phrases rather than from the weighted sum of aword set representation.
other,7-6-A94-1011,bq	We investigate how sets of individually high-precision <term> rules </term> can result in a <term> low precision </term> when used together , and develop some theory about these probably-correct <term> rules </term> .	#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.
measure(ment),12-6-A94-1011,bq	We investigate how sets of individually high-precision <term> rules </term> can result in a <term> low precision </term> when used together , and develop some theory about these probably-correct <term> rules </term> .	#20081 We investigate how sets of individually high-precision rules can result in alow precision when used together, and develop some theory about these probably-correct rules.
other,25-6-A94-1011,bq	We investigate how sets of individually high-precision <term> rules </term> can result in a <term> low precision </term> when used together , and develop some theory about these probably-correct <term> rules </term> .	#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.
tech,10-7-A94-1011,bq	We then proceed to repeat results which show that standard <term> statistical models </term> are not particularly suitable for exploiting <term> linguistically sophisticated representations </term> , and show that a <term> statistically fitted rule-based model </term> provides significantly improved performance for sophisticated <term> representations </term> .	#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.
other,18-7-A94-1011,bq	We then proceed to repeat results which show that standard <term> statistical models </term> are not particularly suitable for exploiting <term> linguistically sophisticated representations </term> , and show that a <term> statistically fitted rule-based model </term> provides significantly improved performance for sophisticated <term> representations </term> .	#20114 We then proceed to repeat results which show that standard statistical models are not particularly suitable for exploitinglinguistically sophisticated representations, and show that a statistically fitted rule-based model provides significantly improved performance for sophisticated representations.
tech,26-7-A94-1011,bq	We then proceed to repeat results which show that standard <term> statistical models </term> are not particularly suitable for exploiting <term> linguistically sophisticated representations </term> , and show that a <term> statistically fitted rule-based model </term> provides significantly improved performance for sophisticated <term> representations </term> .	#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,36-7-A94-1011,bq	We then proceed to repeat results which show that standard <term> statistical models </term> are not particularly suitable for exploiting <term> linguistically sophisticated representations </term> , and show that a <term> statistically fitted rule-based model </term> provides significantly improved performance for sophisticated <term> representations </term> .	#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.
tech,4-8-A94-1011,bq	It therefore shows that <term> statistical systems </term> can exploit <term> sophisticated representations of documents </term> , and lends some support to the use of more <term> linguistically sophisticated representations </term> for <term> document classification </term> .	#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.
other,8-8-A94-1011,bq	It therefore shows that <term> statistical systems </term> can exploit <term> sophisticated representations of documents </term> , and lends some support to the use of more <term> linguistically sophisticated representations </term> for <term> document classification </term> .	#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.
other,22-8-A94-1011,bq	It therefore shows that <term> statistical systems </term> can exploit <term> sophisticated representations of documents </term> , and lends some support to the use of more <term> linguistically sophisticated representations </term> for <term> document classification </term> .	#20156 It therefore shows that statistical systems can exploit sophisticated representations of documents, and lends some support to the use of morelinguistically sophisticated representations for document classification.
tech,26-8-A94-1011,bq	It therefore shows that <term> statistical systems </term> can exploit <term> sophisticated representations of documents </term> , and lends some support to the use of more <term> linguistically sophisticated representations </term> for <term> document classification </term> .	#20160 It therefore shows that statistical systems can exploit sophisticated representations of documents, and lends some support to the use of more linguistically sophisticated representations fordocument classification.
other,8-9-A94-1011,bq	This paper reports on work done for the <term> LRE project SmTA double check </term> , which is creating a <term> PC based tool </term> to be used in the <term> technical abstracting industry </term> .	#20171 This paper reports on work done for theLRE project SmTA double check, which is creating a PC based tool to be used in the technical abstracting industry.
tech,18-9-A94-1011,bq	This paper reports on work done for the <term> LRE project SmTA double check </term> , which is creating a <term> PC based tool </term> to be used in the <term> technical abstracting industry </term> .	#20181 This paper reports on work done for the LRE project SmTA double check, which is creating aPC based tool to be used in the technical abstracting industry.
other,26-9-A94-1011,bq	This paper reports on work done for the <term> LRE project SmTA double check </term> , which is creating a <term> PC based tool </term> to be used in the <term> technical abstracting industry </term> .	#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.


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