| lr,52-3-A94-1011,bq | A novel method for adding <term> linguistic annotation </term> to <term> corpora </term> is presented which involves using a <term> statistical POS tagger </term> in conjunction with <term> unsupervised structure finding methods </term> to derive notions of <term> noun group </term> , <term> verb group </term> , and so on which is inherently extensible to more sophisticated <term> annotation </term> , and does not require a <term> pre-tagged corpus </term> to fit . | #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. |
| lr,8-3-A94-1011,bq | A novel method for adding <term> linguistic annotation </term> to <term> corpora </term> is presented which involves using a <term> statistical POS tagger </term> in conjunction with <term> unsupervised structure finding methods </term> to derive notions of <term> noun group </term> , <term> verb group </term> , and so on which is inherently extensible to more sophisticated <term> annotation </term> , and does not require a <term> pre-tagged corpus </term> to fit . | #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. |
| 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,11-5-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> . | #20056 This leads us to consider the assignment of descriptors from individualphrases rather than from the weighted sum of a word set representation. |
| other,15-4-A94-1011,bq | One of the distinguishing features of a more <term> linguistically sophisticated representation of documents </term> over a <term> word set based representation </term> of them is that <term> linguistically sophisticated units </term> are more frequently individually good predictors of <term> document descriptors ( keywords ) </term> than single <term> words </term> are . | #20017 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. |
| 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,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. |
| 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,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. |
| other,23-4-A94-1011,bq | One of the distinguishing features of a more <term> linguistically sophisticated representation of documents </term> over a <term> word set based representation </term> of them is that <term> linguistically sophisticated units </term> are more frequently individually good predictors of <term> document descriptors ( keywords ) </term> than single <term> words </term> are . | #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. |
| 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. |
| 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. |
| other,29-3-A94-1011,bq | A novel method for adding <term> linguistic annotation </term> to <term> corpora </term> is presented which involves using a <term> statistical POS tagger </term> in conjunction with <term> unsupervised structure finding methods </term> to derive notions of <term> noun group </term> , <term> verb group </term> , and so on which is inherently extensible to more sophisticated <term> annotation </term> , and does not require a <term> pre-tagged corpus </term> to fit . | #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. |
| other,32-3-A94-1011,bq | A novel method for adding <term> linguistic annotation </term> to <term> corpora </term> is presented which involves using a <term> statistical POS tagger </term> in conjunction with <term> unsupervised structure finding methods </term> to derive notions of <term> noun group </term> , <term> verb group </term> , and so on which is inherently extensible to more sophisticated <term> annotation </term> , and does not require a <term> pre-tagged corpus </term> to fit . | #19977 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,33-4-A94-1011,bq | One of the distinguishing features of a more <term> linguistically sophisticated representation of documents </term> over a <term> word set based representation </term> of them is that <term> linguistically sophisticated units </term> are more 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. |
| 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. |
| other,40-4-A94-1011,bq | One of the distinguishing features of a more <term> linguistically sophisticated representation of documents </term> over a <term> word set based representation </term> of them is that <term> linguistically sophisticated units </term> are more frequently individually good predictors of <term> document descriptors ( keywords ) </term> than single <term> words </term> are . | #20042 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 of document descriptors (keywords) than singlewords are. |
| other,45-3-A94-1011,bq | A novel method for adding <term> linguistic annotation </term> to <term> corpora </term> is presented which involves using a <term> statistical POS tagger </term> in conjunction with <term> unsupervised structure finding methods </term> to derive notions of <term> noun group </term> , <term> verb group </term> , and so on which is inherently extensible to more sophisticated <term> annotation </term> , and does not require a <term> pre-tagged corpus </term> to fit . | #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. |
| other,5-3-A94-1011,bq | A novel method for adding <term> linguistic annotation </term> to <term> corpora </term> is presented which involves using a <term> statistical POS tagger </term> in conjunction with <term> unsupervised structure finding methods </term> to derive notions of <term> noun group </term> , <term> verb group </term> , and so on which is inherently extensible to more sophisticated <term> annotation </term> , and does not require a <term> pre-tagged corpus </term> to fit . | #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,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. |
