Concordance

Query E06-1035 20 >
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other,70-5-E06-1035,bq	such as <term> cue phrases </term> and <term>	overlapping speech	</term> , are better indicators for the top-level	#10596 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 andoverlapping speech, are better indicators for the top-level prediction task.
other,19-6-E06-1035,bq	negative impact on models that combine <term>	lexical-cohesion and conversational features	</term> , but do not change the general preference	#10627 We also find that the transcription errors inevitable in ASR output have a negative impact on models that combinelexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.
other,62-5-E06-1035,bq	features </term> performs best , and ( 3 ) <term>	conversational cues	</term> , such as <term> cue phrases </term>	#10588 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,24-5-E06-1035,bq	distinct tasks : ( 1 ) for predicting <term>	subtopic boundaries	</term> , the <term> lexical cohesion-based	#10550 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predictingsubtopic 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,41-5-E06-1035,bq	achieve competitive results , ( 2 ) for <term>	predicting top-level boundaries	</term> , the <term> machine learning approach	#10567 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) forpredicting 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,14-4-E06-1035,bq	<term> ASR output </term> as opposed to <term>	human transcription	</term> . Examination of the effect of <term>	#10523 We then explore the impact on performance of using ASR output as opposed tohuman transcription.
other,14-1-E06-1035,bq	predicting <term> segment boundaries </term> in <term>	spoken multiparty dialogue	</term> . We extend prior work in two ways	#10476 In this paper, we investigate the problem of automatically predicting segment boundaries inspoken multiparty dialogue.
other,17-3-E06-1035,bq	topic shifts </term> to the problem of <term>	identifying subtopic boundaries	</term> . We then explore the impact on <term>	#10505 We first apply approaches that have been proposed for predicting top-level topic shifts to the problem ofidentifying subtopic boundaries.
tech,28-5-E06-1035,bq	<term> subtopic boundaries </term> , the <term>	lexical cohesion-based approach	</term> alone can achieve competitive results	#10554 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, thelexical 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,67-5-E06-1035,bq	conversational cues </term> , such as <term>	cue phrases	</term> and <term> overlapping speech </term>	#10593 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 ascue phrases and overlapping speech, are better indicators for the top-level prediction task.
other,8-5-E06-1035,bq	of <term> features </term> shows that <term>	predicting top-level and predicting subtopic boundaries	</term> are two distinct tasks : ( 1 ) for	#10534 Examination of the effect of features shows thatpredicting 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,9-4-E06-1035,bq	on <term> performance </term> of using <term>	ASR output	</term> as opposed to <term> human transcription	#10518 We then explore the impact on performance of usingASR output as opposed to human transcription.
other,9-6-E06-1035,bq	transcription errors </term> inevitable in <term>	ASR output	</term> have a negative impact on models	#10617 We also find that the transcription errors inevitable inASR 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.
other,11-1-E06-1035,bq	problem of automatically predicting <term>	segment boundaries	</term> in <term> spoken multiparty dialogue	#10473 In this paper, we investigate the problem of automatically predictingsegment boundaries in spoken multiparty dialogue.
other,5-6-E06-1035,bq	prediction task . We also find that the <term>	transcription errors	</term> inevitable in <term> ASR output </term>	#10613 We also find that thetranscription 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.
measure(ment),6-4-E06-1035,bq	</term> . We then explore the impact on <term>	performance	</term> of using <term> ASR output </term> as	#10515 We then explore the impact onperformance of using ASR output as opposed to human transcription.
other,51-5-E06-1035,bq	learning approach </term> that combines <term>	lexical-cohesion and conversational features	</term> performs best , and ( 3 ) <term> conversational	#10577 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 combineslexical-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,5-5-E06-1035,bq	</term> . Examination of the effect of <term>	features	</term> shows that <term> predicting top-level	#10531 Examination of the effect offeatures 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.
tech,46-5-E06-1035,bq	predicting top-level boundaries </term> , the <term>	machine learning approach	</term> that combines <term> lexical-cohesion	#10572 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, themachine 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,9-3-E06-1035,bq	approaches that have been proposed for <term>	predicting top-level topic shifts	</term> to the problem of <term> identifying	#10497 We first apply approaches that have been proposed forpredicting top-level topic shifts to the problem of identifying subtopic boundaries.


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