| tech,3-1-A94-1011,ak | The use of <term> NLP techniques </term> for <term> document classification </term> has not produced significant improvements in performance within the standard <term> term weighting statistical assignment paradigm </term> ( Fagan 1987 ; Lewis , 1992bc ; Buckley , 1993 ) . | #25771 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,6-1-A94-1011,ak | The use of <term> NLP techniques </term> for <term> document classification </term> has not produced significant improvements in performance within the standard <term> term weighting statistical assignment paradigm </term> ( Fagan 1987 ; Lewis , 1992bc ; Buckley , 1993 ) . | #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,18-1-A94-1011,ak | The use of <term> NLP techniques </term> for <term> document classification </term> has not produced significant improvements in performance within the standard <term> term weighting statistical assignment paradigm </term> ( Fagan 1987 ; Lewis , 1992bc ; Buckley , 1993 ) . | #25786 The use of NLP techniques for document classification has not produced significant improvements in performance within the standardterm weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992bc; Buckley, 1993). | |
| tech,16-2-A94-1011,ak | This perplexing fact needs both an explanation and a solution if the power of recently developed <term> NLP techniques </term> are to be successfully applied in <term> IR </term> . | #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. | |
| tech,24-2-A94-1011,ak | This perplexing fact needs both an explanation and a solution if the power of recently developed <term> NLP techniques </term> are to be successfully applied in <term> IR </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. | |
| tech,5-3-A94-1011,ak | 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 . | #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. | |
| lr,8-3-A94-1011,ak | 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 . | #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. | |
| tech,15-3-A94-1011,ak | 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 . | #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,21-3-A94-1011,ak | 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 . | #25851 A novel method for adding linguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction withunsupervised 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,29-3-A94-1011,ak | 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 . | #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 | 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 . | #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. | |
| tech,45-3-A94-1011,ak | 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 . | #25875 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,52-3-A94-1011,ak | 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 . | #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. | |
| other,8-4-A94-1011,ak | One of the distinguishing features of a more <term> linguistically sophisticated representation </term> of <term> documents </term> over a <term> word set based representation </term> of them is that linguistically sophisticated units are more frequently individually good predictors of <term> document descriptors ( keywords ) </term> than single <term> words </term> are . | #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,12-4-A94-1011,ak | One of the distinguishing features of a more <term> linguistically sophisticated representation </term> of <term> documents </term> over a <term> word set based representation </term> of them is that linguistically sophisticated units are more frequently individually good predictors of <term> document descriptors ( keywords ) </term> than single <term> words </term> are . | #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 | One of the distinguishing features of a more <term> linguistically sophisticated representation </term> of <term> documents </term> over a <term> word set based representation </term> of them is that linguistically sophisticated units are more frequently individually good predictors of <term> document descriptors ( keywords ) </term> than single <term> words </term> are . | #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,33-4-A94-1011,ak | One of the distinguishing features of a more <term> linguistically sophisticated representation </term> of <term> documents </term> over a <term> word set based representation </term> of them is that linguistically sophisticated units are more 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,40-4-A94-1011,ak | One of the distinguishing features of a more <term> linguistically sophisticated representation </term> of <term> documents </term> over a <term> word set based representation </term> of them is that linguistically sophisticated units are more frequently individually good predictors of <term> document descriptors ( keywords ) </term> than single <term> words </term> are . | #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. | |
| other,8-5-A94-1011,ak | 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> . | #25938 This leads us to consider the assignment ofdescriptors from individual phrases rather than from the weighted sum of a word set representation. | |
| other,11-5-A94-1011,ak | 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> . | #25941 This leads us to consider the assignment of descriptors from individualphrases rather than from the weighted sum of a word set representation. |
