Examination of the effect of
<term>
features
</term>
shows that predicting top-level and predicting
<term>
subtopic boundaries
</term>
are two distinct tasks : ( 1 ) for predicting
<term>
subtopic boundaries
</term>
, the
<term>
lexical cohesion-based approach
</term>
alone can achieve competitive results , ( 2 ) for predicting
<term>
top-level boundaries
</term>
, the
<term>
machine learning approach
</term>
that combines
<term>
lexical-cohesion and conversational features
</term>
performs best , and ( 3 )
<term>
conversational cues
</term>
, such as
<term>
cue phrases
</term>
and
<term>
overlapping speech
</term>
, are better indicators for the
<term>
top-level prediction task
</term>
.
#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.