| other,18-3-E06-1035,ak | </term> to the problem of identifying <term> | subtopic boundaries | </term> . We then explore the impact on | #11443 We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries . | |
| other,12-5-E06-1035,ak | predicting top-level and predicting <term> | subtopic boundaries | </term> are two distinct tasks : ( 1 ) for | #11475 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. | |
| other,24-5-E06-1035,ak | distinct tasks : ( 1 ) for predicting <term> | subtopic boundaries | </term> , the <term> lexical cohesion-based | #11487 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries , the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. | |
| other,42-5-E06-1035,ak | competitive results , ( 2 ) for predicting <term> | top-level boundaries | </term> , the <term> machine learning approach | #11505 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries , the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. | |
| other,62-5-E06-1035,ak | features </term> performs best , and ( 3 ) <term> | conversational cues | </term> , such as <term> cue phrases </term> | #11525 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues , such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. |
