| 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,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,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. |
| measure(ment),12-6-A94-1011,bq | high-precision <term> rules </term> can result in a <term> | low precision | </term> when used together , and develop | #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 | 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. |
| 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. |
| other,18-7-A94-1011,bq | particularly suitable for exploiting <term> | linguistically sophisticated representations | </term> , and show that a <term> statistically | #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 | 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,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. |
| 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. |
| 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. |
| other,22-8-A94-1011,bq | 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 | sophisticated representations </term> for <term> | document classification | </term> . This paper reports on work done | #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 | paper reports on work done for the <term> | LRE project SmTA double check | </term> , which is creating a <term> PC based | #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 | check </term> , which is creating a <term> | PC based tool | </term> to be used in the <term> technical | #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 | 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. |
