| other,51-5-E06-1035,ak | learning approach </term> that combines <term> | lexical-cohesion and conversational features | </term> performs best , and ( 3 ) <term> conversational | #11514 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,19-6-E06-1035,ak | on <term> models </term> that combine <term> | lexical-cohesion and conversational features | </term> , but do not change the general | #11564 We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features , but do not change the general preference of approach for the two tasks. |
