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<Paper uid="W04-2808">
  <Title>Making Relative Sense: From Word-graphs to Semantic Frames</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> We differentiate between controlled single-domain and more conversational multi-domain spoken dialogue systems (Allen et al., 2001). The transition from the former to the later can be regarded as a scaling process, since virtually every processing technique applicable for restricted single domain user utterances has to be adopted to new challenges, i.e., varying context-dependencies (Porzel et al., 2004) increasing levels of ambiguity (Gurevych et al., 2003a; Loos and Porzel, 2004) and less predictable input (Loeckelt et al., 2002). Additionally, for conversational multi-domain spoken dialogue systems tasks have to be tackled that were by and large unnecessary in restricted single-domain systems. In this exploration, we will focus on a subset of these tasks, namely: a2 hypotheses verification (HV) - i.e. finding the best hypothesis out of a set of possible speech recognition hypotheses (SRH); a2 sense disambiguation (SD) - i.e. determining the best mapping of the lexically ambiguous linguistic forms contained therein to their sense-specific semantic representations; a2 relation tagging (RT) - i.e. determining adequate semantic relations between the relevant sense-tagged entities.</Paragraph>
    <Paragraph position="1"> Many of these tasks have been addressed in other fields, for example, hypothesis verification in the field of machine translation (Tran et al., 1996), sense disambiguation in speech synthesis (Yarowsky, 1995), and relation tagging in information retrieval (Marsh and Perzanowski, 1999). These challenges also apply for spoken dialogue systems and arise when they are scaled up towards multi-domain and more conversational settings.</Paragraph>
    <Paragraph position="2"> In this paper we will address the utility of using ontologically modeled knowledge to assist in solving these tasks in spoken dialogue systems. Following an overview of the state of the art in Section 2 and the ontology-based coherence scoring system in Section 3, we describe its employment in the task of hypotheses verification in Section 4. In Section 5 we describe the system's employment for the task of sense disambiguation and in Section 6 we present first results of a study examining the performance of the system for the task of relation tagging. An analysis of the evaluation results and concluding remarks are given in Section 7.</Paragraph>
  </Section>
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