| other,2-3-P95-1025,ak | Using the <term> definition-based conceptual co-occurrence data </term> collected from the relatively small <term> Brown corpus </term> , our <term> sense disambiguation system </term> achieves an <term> average accuracy </term> comparable to <term> human performance </term> given the same <term> contextual information </term> . | #27750 Using thedefinition-based conceptual co-occurrence data collected from the relatively small Brown corpus, our sense disambiguation system achieves an average accuracy comparable to human performance given the same contextual information. | |
| lr,13-2-P95-1025,ak | In this paper , we describe an approach which overcomes this problem using <term> dictionary definitions </term> . | #27745 In this paper, we describe an approach which overcomes this problem usingdictionary definitions. | |
| measure(ment),20-3-P95-1025,ak | Using the <term> definition-based conceptual co-occurrence data </term> collected from the relatively small <term> Brown corpus </term> , our <term> sense disambiguation system </term> achieves an <term> average accuracy </term> comparable to <term> human performance </term> given the same <term> contextual information </term> . | #27768 Using the definition-based conceptual co-occurrence data collected from the relatively small Brown corpus, our sense disambiguation system achieves anaverage accuracy comparable to human performance given the same contextual information. | |
| measure(ment),24-3-P95-1025,ak | Using the <term> definition-based conceptual co-occurrence data </term> collected from the relatively small <term> Brown corpus </term> , our <term> sense disambiguation system </term> achieves an <term> average accuracy </term> comparable to <term> human performance </term> given the same <term> contextual information </term> . | #27772 Using the definition-based conceptual co-occurrence data collected from the relatively small Brown corpus, our sense disambiguation system achieves an average accuracy comparable tohuman performance given the same contextual information. | |
| tech,0-1-P95-1025,ak | Our objectives are met by giving clear definitions that determine the <term> projection </term> of <term> structures </term> from the <term> lexicon </term> , and identify <term> maximal projections </term> , <term> auxiliary trees </term> and <term> foot nodes </term> . <term> Corpus-based sense disambiguation methods </term> , like most other <term> statistical NLP approaches </term> , suffer from the problem of <term> data sparseness </term> . | #27712 Our objectives are met by giving clear definitions that determine the projection of structures from the lexicon, and identify maximal projections, auxiliary trees and foot nodes.Corpus-based sense disambiguation methods, like most other statistical NLP approaches, suffer from the problem of data sparseness. | |
| lr-prod,11-3-P95-1025,ak | Using the <term> definition-based conceptual co-occurrence data </term> collected from the relatively small <term> Brown corpus </term> , our <term> sense disambiguation system </term> achieves an <term> average accuracy </term> comparable to <term> human performance </term> given the same <term> contextual information </term> . | #27759 Using the definition-based conceptual co-occurrence data collected from the relatively smallBrown corpus, our sense disambiguation system achieves an average accuracy comparable to human performance given the same contextual information. | |
| other,17-1-P95-1025,ak | <term> Corpus-based sense disambiguation methods </term> , like most other <term> statistical NLP approaches </term> , suffer from the problem of <term> data sparseness </term> . | #27729 Corpus-based sense disambiguation methods, like most other statistical NLP approaches, suffer from the problem ofdata sparseness. | |
| tech,8-1-P95-1025,ak | <term> Corpus-based sense disambiguation methods </term> , like most other <term> statistical NLP approaches </term> , suffer from the problem of <term> data sparseness </term> . | #27720 Corpus-based sense disambiguation methods, like most otherstatistical NLP approaches, suffer from the problem of data sparseness. | |
| tech,15-3-P95-1025,ak | Using the <term> definition-based conceptual co-occurrence data </term> collected from the relatively small <term> Brown corpus </term> , our <term> sense disambiguation system </term> achieves an <term> average accuracy </term> comparable to <term> human performance </term> given the same <term> contextual information </term> . | #27763 Using the definition-based conceptual co-occurrence data collected from the relatively small Brown corpus, oursense disambiguation system achieves an average accuracy comparable to human performance given the same contextual information. | |
| other,29-3-P95-1025,ak | Using the <term> definition-based conceptual co-occurrence data </term> collected from the relatively small <term> Brown corpus </term> , our <term> sense disambiguation system </term> achieves an <term> average accuracy </term> comparable to <term> human performance </term> given the same <term> contextual information </term> . | #27777 Using the definition-based conceptual co-occurrence data collected from the relatively small Brown corpus, our sense disambiguation system achieves an average accuracy comparable to human performance given the samecontextual information. |
