Positive filter
NN, CC, JJ, NNS 2
(64.0 per million)
other,51-5-E06-1035,ak
learning approach
</term>
that combines
<term>
lexical-cohesion and conversational features
</term>
performs best , and ( 3 )
<term>
conversational
#11514Examination 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
#11564We 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.