| other,45-3-A94-1011,bq | inherently extensible to more sophisticated <term> | annotation | </term> , and does not require a <term> pre-tagged | #19990 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 sophisticatedannotation, and does not require a pre-tagged corpus to fit. |
| lr,8-3-A94-1011,bq | <term> linguistic annotation </term> to <term> | corpora | </term> is presented which involves using | #19953 A novel method for adding linguistic annotation tocorpora 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,8-5-A94-1011,bq | leads us to consider the assignment of <term> | descriptors | </term> from individual <term> phrases </term> | #20053 This leads us to consider the assignment ofdescriptors from individual phrases rather than from the weighted sum of a word set representation. |
| tech,6-1-A94-1011,bq | use of <term> NLP techniques </term> for <term> | document classification | </term> has not produced significant improvements | #19889 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,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,33-4-A94-1011,bq | frequently individually good predictors of <term> | document descriptors ( keywords ) | </term> than single <term> words </term> are | #20035 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 ofdocument descriptors ( keywords ) than single words are. |
| tech,24-2-A94-1011,bq | </term> are to be successfully applied in <term> | IR | </term> . A novel method for adding <term> | #19943 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,5-3-A94-1011,bq | </term> . A novel method for adding <term> | linguistic annotation | </term> to <term> corpora </term> is presented | #19950 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. |
| other,8-4-A94-1011,bq | distinguishing features of a more <term> | linguistically sophisticated representation of documents | </term> over a <term> word set based representation | #20010 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,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. |
| 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. |
| other,23-4-A94-1011,bq | representation </term> of them is that <term> | linguistically sophisticated units | </term> are more frequently individually | #20025 One of the distinguishing features of a more linguistically sophisticated representation of documents over a word set based representation of them is thatlinguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords) than single words are. |
| 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,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,3-1-A94-1011,bq | translation </term> use . The use of <term> | NLP techniques | </term> for <term> document classification </term> | #19886 The use ofNLP techniques for document classification has not produced significant improvements in performance within the standard term weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992bc; Buckley, 1993). |
| tech,16-2-A94-1011,bq | if the power of recently developed <term> | NLP techniques | </term> are to be successfully applied in | #19935 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,29-3-A94-1011,bq | methods </term> to derive notions of <term> | noun group | </term> , <term> verb group </term> , and so | #19974 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 ofnoun 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,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,11-5-A94-1011,bq | <term> descriptors </term> from individual <term> | phrases | </term> rather than from the <term> weighted | #20056 This leads us to consider the assignment of descriptors from individualphrases rather than from the weighted sum of a word set representation. |
| lr,52-3-A94-1011,bq | annotation </term> , and does not require a <term> | pre-tagged corpus | </term> to fit . One of the distinguishing | #19997 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. |
