Positive filter
conversational, cues 1
(32.0 per million)
other,62-5-E06-1035,ak
features
</term>
performs best , and ( 3 )
<term>
conversational cues
</term>
, such as
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cue phrases
</term>
#11525Examination 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.