| other,23-4-N03-1012,ak | An evaluation of our <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term> German corpus </term> of 2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) . | #2525 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,ak | An evaluation of our <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term> German corpus </term> of 2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) . | #2534 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,ak | An evaluation of our <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term> German corpus </term> of 2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) . | #2521 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,ak | We apply our <term> system </term> to the task of scoring alternative <term> speech recognition hypotheses ( SRH ) </term> in terms of their <term> semantic coherence </term> . | #2467 We apply our system to the task of scoring alternativespeech recognition hypotheses ( SRH ) in terms of their semantic coherence. | |
| other,3-3-N03-1012,ak | We conducted an <term> annotation experiment </term> and showed that <term> human annotators </term> can reliably differentiate between semantically coherent and incoherent <term> speech recognition hypotheses </term> . | #2483 We conducted anannotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. | |
| tech,19-1-N03-1012,ak | In this paper we present <term> ONTOSCORE </term> , a system for scoring sets of <term> concepts </term> on the basis of an <term> ontology </term> . | #2455 In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of anontology. | |
| other,18-3-N03-1012,ak | We conducted an <term> annotation experiment </term> and showed that <term> human annotators </term> can reliably differentiate between semantically coherent and incoherent <term> speech recognition hypotheses </term> . | #2498 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,ak | In this paper we present <term> ONTOSCORE </term> , a system for scoring sets of <term> concepts </term> on the basis of an <term> ontology </term> . | #2449 In this paper we present ONTOSCORE, a system for scoring sets ofconcepts on the basis of an ontology. | |
| tech,3-2-N03-1012,ak | We apply our <term> system </term> to the task of scoring alternative <term> speech recognition hypotheses ( SRH ) </term> in terms of their <term> semantic coherence </term> . | #2460 We apply oursystem to the task of scoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence. | |
| tech,4-4-N03-1012,ak | An evaluation of our <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term> German corpus </term> of 2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) . | #2506 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,ak | In this paper we present <term> ONTOSCORE </term> , a system for scoring sets of <term> concepts </term> on the basis of an <term> ontology </term> . | #2441 In this paper we presentONTOSCORE, a system for scoring sets of concepts on the basis of an ontology. | |
| other,8-3-N03-1012,ak | We conducted an <term> annotation experiment </term> and showed that <term> human annotators </term> can reliably differentiate between semantically coherent and incoherent <term> speech recognition hypotheses </term> . | #2488 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,ak | An evaluation of our <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term> German corpus </term> of 2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) . | #2509 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,ak | We apply our <term> system </term> to the task of scoring alternative <term> speech recognition hypotheses ( SRH ) </term> in terms of their <term> semantic coherence </term> . | #2477 We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of theirsemantic coherence. |
