Semantic coherence scoring using an ontology

Abstract

In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of an ontology. We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence. We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. 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 a baseline of 54.55%).