| other,32-3-A94-1011,ak | notions of <term> noun group </term> , <term> | verb group | </term> , and so on which is inherently extensible | #25862 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,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. | |
| other,15-4-A94-1011,ak | </term> of <term> documents </term> over a <term> | word set based representation | </term> of them is that linguistically sophisticated | #25902 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. | |
| lr,52-3-A94-1011,ak | annotation </term> , and does not require a <term> | pre-tagged corpus | </term> to fit . One of the distinguishing | #25882 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 apre-tagged corpus to fit. | |
| 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. | |
| tech,15-3-A94-1011,ak | is presented which involves using a <term> | statistical POS tagger | </term> in conjunction with <term> unsupervised | #25845 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,5-3-A94-1011,ak | </term> . A novel method for adding <term> | linguistic annotation | </term> to <term> corpora </term> is presented | #25835 A novel method for addinglinguistic 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. | |
| tech,16-2-A94-1011,ak | if the power of recently developed <term> | NLP techniques | </term> are to be successfully applied in | #25820 This perplexing fact needs both an explanation and a solution if the power of recently developedNLP techniques are to be successfully applied in IR. | |
| 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,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. | |
| 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,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. | |
| tech,6-1-A94-1011,ak | use of <term> NLP techniques </term> for <term> | document classification | </term> has not produced significant improvements | #25774 The use of NLP techniques fordocument classification has not produced significant improvements in performance within the standard term weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992bc; Buckley, 1993). | |
| tech,24-2-A94-1011,ak | </term> are to be successfully applied in <term> | IR | </term> . A novel method for adding <term> | #25828 This perplexing fact needs both an explanation and a solution if the power of recently developed NLP techniques are to be successfully applied inIR. | |
| other,11-5-A94-1011,ak | <term> descriptors </term> from individual <term> | phrases | </term> rather than from the <term> weighted | #25941 This leads us to consider the assignment of descriptors from individualphrases rather than from the weighted sum of a word 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. | |
| other,8-4-A94-1011,ak | distinguishing features of a more <term> | linguistically sophisticated representation | </term> of <term> documents </term> over a <term> | #25895 One of the distinguishing features of a morelinguistically 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 single words are. | |
| 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. | |
| other,8-5-A94-1011,ak | leads us to consider the assignment of <term> | descriptors | </term> from individual <term> phrases </term> | #25938 This leads us to consider the assignment ofdescriptors from individual phrases rather than from the weighted sum of a word set representation. |
