Concordance

Query N03-1012 15 >
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other,23-4-N03-1012,bq	<term> German corpus </term> of 2.284 <term>	SRHs	</term> as either coherent or incoherent	#2524 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284SRHs as either coherent or incoherent (given a baseline of 54.55%).
measure(ment),32-4-N03-1012,bq	either coherent or incoherent ( given a <term>	baseline	</term> of 54.55 % ) . We propose a new <term>	#2533 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given abaseline of 54.55%).
lr,19-4-N03-1012,bq	successfully classifies 73.2 % in a <term>	German corpus	</term> of 2.284 <term> SRHs </term> as either	#2520 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in aGerman corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%).
other,10-2-N03-1012,bq	of <term> scoring </term> alternative <term>	speech recognition hypotheses ( SRH )	</term> in terms of their <term> semantic coherence	#2466 We apply our system to the task of scoring alternativespeech recognition hypotheses ( SRH ) in terms of their semantic coherence.
other,3-3-N03-1012,bq	coherence </term> . We conducted an <term>	annotation experiment	</term> and showed that <term> human annotators	#2482 We conducted anannotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses.
other,19-1-N03-1012,bq	<term> concepts </term> on the basis of an <term>	ontology	</term> . We apply our <term> system </term>	#2454 In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of anontology.
other,18-3-N03-1012,bq	semantically coherent and incoherent <term>	speech recognition hypotheses	</term> . An evaluation of our <term> system	#2497 We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherentspeech recognition hypotheses.
other,13-1-N03-1012,bq	</term> , a system for scoring sets of <term>	concepts	</term> on the basis of an <term> ontology </term>	#2448 In this paper we present ONTOSCORE, a system for scoring sets ofconcepts on the basis of an ontology.
other,8-2-N03-1012,bq	our <term> system </term> to the task of <term>	scoring	</term> alternative <term> speech recognition	#2464 We apply our system to the task ofscoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence.
tech,3-2-N03-1012,bq	<term> ontology </term> . We apply our <term>	system	</term> to the task of <term> scoring </term>	#2459 We apply oursystem to the task of scoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence.
tech,4-4-N03-1012,bq	hypotheses </term> . An evaluation of our <term>	system	</term> against the <term> annotated data </term>	#2505 An evaluation of oursystem against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%).
tool,5-1-N03-1012,bq	metric </term> . In this paper we present <term>	ONTOSCORE	</term> , a system for scoring sets of <term>	#2440 In this paper we presentONTOSCORE, a system for scoring sets of concepts on the basis of an ontology.
other,8-3-N03-1012,bq	annotation experiment </term> and showed that <term>	human annotators	</term> can reliably differentiate between	#2487 We conducted an annotation experiment and showed thathuman annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses.
lr,7-4-N03-1012,bq	our <term> system </term> against the <term>	annotated data	</term> shows that , it successfully classifies	#2508 An evaluation of our system against theannotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%).
other,20-2-N03-1012,bq	hypotheses ( SRH ) </term> in terms of their <term>	semantic coherence	</term> . We conducted an <term> annotation	#2476 We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of theirsemantic coherence.


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