| 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. | |
| lr,8-3-A94-1011,ak | <term> linguistic annotation </term> to <term> | corpora | </term> is presented which involves using | #25838 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),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. | |
| 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. | |
| 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. | |
| 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,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. | |
| 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. | |
| 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. | |
| 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,12-4-A94-1011,ak | sophisticated representation </term> of <term> | documents | </term> over a <term> word set based representation | #25899 One of the distinguishing features of a more linguistically sophisticated representation ofdocuments 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,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. | |
| 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,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,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,29-3-A94-1011,ak | methods </term> to derive notions of <term> | noun group | </term> , <term> verb group </term> , and so | #25859 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,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,33-4-A94-1011,ak | frequently individually good predictors of <term> | document descriptors ( keywords ) | </term> than single <term> words </term> are | #25920 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,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. | |
| other,40-4-A94-1011,ak | descriptors ( keywords ) </term> than single <term> | words | </term> are . This leads us to consider the | #25927 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. |
