#11530Examination 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,11-1-E06-1035,ak
problem of automatically predicting
<term>
segment boundaries
</term>
in
<term>
spoken multiparty dialogue
#11410In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue.
other,5-6-E06-1035,ak
task
</term>
. We also find that the
<term>
transcription errors
</term>
inevitable in
<term>
ASR output
</term>
#11550We 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.