Concordance

Query E06-1035 21 (672.3 per million)

other,11-1-E06-1035,ak	problem of automatically predicting <term>	segment boundaries	</term> in <term> spoken multiparty dialogue	#11410 In this paper, we investigate the problem of automatically predictingsegment boundaries in spoken multiparty dialogue.
other,14-1-E06-1035,ak	predicting <term> segment boundaries </term> in <term>	spoken multiparty dialogue	</term> . We extend prior work in two ways	#11413 In this paper, we investigate the problem of automatically predicting segment boundaries inspoken multiparty dialogue.
other,10-3-E06-1035,ak	have been proposed for predicting <term>	top-level topic shifts	</term> to the problem of identifying <term>	#11435 We first apply approaches that have been proposed for predictingtop-level topic shifts to the problem of identifying subtopic boundaries.
other,18-3-E06-1035,ak	</term> to the problem of identifying <term>	subtopic boundaries	</term> . We then explore the impact on performance	#11443 We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifyingsubtopic boundaries.
other,9-4-E06-1035,ak	the impact on performance of using <term>	ASR output	</term> as opposed to <term> human transcription	#11455 We then explore the impact on performance of usingASR output as opposed to human transcription.
other,14-4-E06-1035,ak	<term> ASR output </term> as opposed to <term>	human transcription	</term> . Examination of the effect of <term>	#11460 We then explore the impact on performance of using ASR output as opposed tohuman transcription.
other,5-5-E06-1035,ak	</term> . Examination of the effect of <term>	features	</term> shows that predicting top-level and	#11468 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.
other,12-5-E06-1035,ak	predicting top-level and predicting <term>	subtopic boundaries	</term> are two distinct tasks : ( 1 ) for	#11475 Examination of the effect of features shows that predicting top-level and predictingsubtopic 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,ak	distinct tasks : ( 1 ) for predicting <term>	subtopic boundaries	</term> , the <term> lexical cohesion-based	#11487 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.
tech,28-5-E06-1035,ak	<term> subtopic boundaries </term> , the <term>	lexical cohesion-based approach	</term> alone can achieve competitive results	#11491 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,42-5-E06-1035,ak	competitive results , ( 2 ) for predicting <term>	top-level boundaries	</term> , the <term> machine learning approach	#11505 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 predictingtop-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,ak	<term> top-level boundaries </term> , the <term>	machine learning approach	</term> that combines <term> lexical-cohesion	#11509 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,51-5-E06-1035,ak	learning approach </term> that combines <term>	lexical-cohesion and conversational features	</term> performs best , and ( 3 ) <term> conversational	#11514 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,62-5-E06-1035,ak	features </term> performs best , and ( 3 ) <term>	conversational cues	</term> , such as <term> cue phrases </term>	#11525 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,67-5-E06-1035,ak	conversational cues </term> , such as <term>	cue phrases	</term> and <term> overlapping speech </term>	#11530 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,70-5-E06-1035,ak	such as <term> cue phrases </term> and <term>	overlapping speech	</term> , are better indicators for the <term>	#11533 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,78-5-E06-1035,ak	</term> , are better indicators for the <term>	top-level prediction task	</term> . We also find that the <term> transcription	#11541 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 thetop-level prediction task.
other,5-6-E06-1035,ak	task </term> . We also find that the <term>	transcription errors	</term> inevitable in <term> ASR output </term>	#11550 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.
other,9-6-E06-1035,ak	transcription errors </term> inevitable in <term>	ASR output	</term> have a negative impact on <term> models	#11554 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.
model,16-6-E06-1035,ak	output </term> have a negative impact on <term>	models	</term> that combine <term> lexical-cohesion	#11561 We also find that the transcription errors inevitable in ASR output have a negative impact onmodels that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.


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