| measure(ment),16-5-A94-1011,ak | phrases </term> rather than from the <term> | weighted sum | </term> of a <term> word set representation | #25946 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,ak | from the <term> weighted sum </term> of a <term> | word set representation | </term> . We investigate how sets of individually | #25950 This leads us to consider the assignment of descriptors from individual phrases rather than from the weighted sum of aword set representation. | |
| model,6-6-A94-1011,ak | investigate how sets of individually <term> | high-precision rules | </term> can result in a low <term> precision | #25960 We investigate how sets of individuallyhigh-precision rules can result in a low precision when used together, and develop some theory about these probably-correct rules. | |
| measure(ment),13-6-A94-1011,ak | high-precision rules </term> can result in a low <term> | precision | </term> when used together , and develop | #25967 We investigate how sets of individually high-precision rules can result in a lowprecision when used together, and develop some theory about these probably-correct rules. | |
| model,25-6-A94-1011,ak | theory about these probably-correct <term> | rules | </term> . We then proceed to repeat results | #25979 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. | |
| model,10-7-A94-1011,ak | repeat results which show that standard <term> | statistical models | </term> are not particularly suitable for | #25991 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,ak | particularly suitable for exploiting <term> | linguistically sophisticated representations | </term> , and show that a <term> statistically | #25999 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. | |
| model,26-7-A94-1011,ak | representations </term> , and show that a <term> | statistically fitted rule-based model | </term> provides significantly improved performance | #26007 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,35-7-A94-1011,ak | significantly improved performance for <term> | sophisticated representations | </term> . It therefore shows that <term> statistical | #26016 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 forsophisticated representations. | |
| tech,4-8-A94-1011,ak | representations </term> . It therefore shows that <term> | statistical systems | </term> can exploit <term> sophisticated representations | #26023 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,ak | statistical systems </term> can exploit <term> | sophisticated representations | </term> of <term> documents </term> , and lends | #26027 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,11-8-A94-1011,ak | sophisticated representations </term> of <term> | documents | </term> , and lends some support to the use | #26030 It therefore shows that statistical systems can exploit sophisticated representations ofdocuments, and lends some support to the use of more linguistically sophisticated representations for document classification. | |
| other,22-8-A94-1011,ak | lends some support to the use of more <term> | linguistically sophisticated representations | </term> for <term> document classification </term> | #26041 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,ak | sophisticated representations </term> for <term> | document classification | </term> . This paper reports on work done | #26045 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. |
