| measure(ment),0-3-H91-1067,ak | in the <term> training corpus </term> . <term> | False positive rates | </term> are one to three percent . Five <term> | #21891 The completeness of the output list increases monotonically with the total occurrences of each verb in the training corpus.False positive rates are one to three percent. | |
| lr,9-1-H91-1067,ak | an implemented program that takes a <term> | tagged text corpus | </term> and generates a partial list of the | #21853 This paper describes an implemented program that takes atagged text corpus and generates a partial list of the subcategorization frames in which each verb occurs. | |
| other,14-2-H91-1067,ak | total <term> occurrences </term> of each <term> | verb | </term> in the <term> training corpus </term> | #21885 The completeness of the output list increases monotonically with the total occurrences of eachverb in the training corpus. | |
| other,24-1-H91-1067,ak | subcategorization frames </term> in which each <term> | verb | </term> occurs . The completeness of the | #21868 This paper describes an implemented program that takes a tagged text corpus and generates a partial list of the subcategorization frames in which eachverb occurs. | |
| other,1-4-H91-1067,ak | </term> are one to three percent . Five <term> | subcategorization frames | </term> are currently detected and we foresee | #21901 Fivesubcategorization frames are currently detected and we foresee no impediment to detecting many more. | |
| lr,8-5-H91-1067,ak | Ultimately , we expect to provide a large <term> | subcategorization dictionary | </term> to the <term> NLP community </term> and | #21924 Ultimately, we expect to provide a largesubcategorization dictionary to the NLP community and to train dictionaries for specific corpora. | |
| lr,20-5-H91-1067,ak | <term> dictionaries </term> for specific <term> | corpora | </term> . A method of <term> sense resolution | #21936 Ultimately, we expect to provide a large subcategorization dictionary to the NLP community and to train dictionaries for specificcorpora. | |
| lr,17-2-H91-1067,ak | </term> of each <term> verb </term> in the <term> | training corpus | </term> . <term> False positive rates </term> | #21888 The completeness of the output list increases monotonically with the total occurrences of each verb in thetraining corpus. | |
| other,19-1-H91-1067,ak | and generates a partial list of the <term> | subcategorization frames | </term> in which each <term> verb </term> occurs | #21863 This paper describes an implemented program that takes a tagged text corpus and generates a partial list of thesubcategorization frames in which each verb occurs. | |
| other,12-5-H91-1067,ak | subcategorization dictionary </term> to the <term> | NLP community | </term> and to train <term> dictionaries </term> | #21928 Ultimately, we expect to provide a large subcategorization dictionary to theNLP community and to train dictionaries for specific corpora. | |
| other,11-2-H91-1067,ak | increases monotonically with the total <term> | occurrences | </term> of each <term> verb </term> in the <term> | #21882 The completeness of the output list increases monotonically with the totaloccurrences of each verb in the training corpus. | |
| lr,17-5-H91-1067,ak | <term> NLP community </term> and to train <term> | dictionaries | </term> for specific <term> corpora </term> . | #21933 Ultimately, we expect to provide a large subcategorization dictionary to the NLP community and to traindictionaries for specific corpora. |
