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<Paper uid="C86-1140">
  <Title>TItE ROLE OF SEMANTIC PROCESSING IN AN AUTOMATIC SPEECH UNDERSTANDING SYSTEM</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> Understanding spoken utterances requires more than mere word recognition. It is based on a number of meaning aspects, covering the range from textual interpretation of a sentence up to the revelation of the speaker's intention in the context of a special dialogue situation. In the speech understanding and dialogue system EVAR /4/, task-independent semantic analysis, domain-dependent pragmatic analysis, and dialogue-specific aspects are implemented in three separate modules /2/.</Paragraph>
    <Paragraph position="1"> Semantic analysis comprises those aspects that can be studied at the isolated sentence, independent from its actual use in the dialogue. The semantics module disregards communicative aspects of an utterance as well as its situational and thematic context. Thus, semantic consistency of words and constituents and underlying relational structure of the sentence are the main points of interest in this stage of analysis. Semantic knowledge consists of lexical meanings of words and selectional restrictions between them. The analysis of the functional structure is based on the principles of case and valency theory.</Paragraph>
    <Paragraph position="2"> 2. Valencies and case theory The theoretical background for the analysis of functional relations in a sentence is given by valency and case theory /5, 3/. The main idea is that the syntactic and the semantic structure of a sentence are essentially determined by its head verb. The property to call for a certain number and kind of complementary noun groups or prepositional groups that are necessary to build up an adequate sentence is called valency.</Paragraph>
    <Paragraph position="3"> The morpho-syntactic and semantic descriptions of the complements constitute a verb frame with slots to be filled up by actual phrases. This valency frame is augmented by case labels circumscribing the functional role of the expected phrase. To give an example, the verb &amp;quot;suchen&amp;quot; (to look for) has the case slots: AGENT: noun group (nominative), ANIMATE, obligatory OBJECT: noun group (accusative), obligatory LOCATION: adverbial group, PLACE, facultative.</Paragraph>
    <Paragraph position="4"> The lexical knowledge base provides caseframe entries for all verbal and nominal items with valency properties. Mostly, meaning alternatives correspond to different caseframes. We use a relatively detailed case system with about 30 cases.</Paragraph>
    <Paragraph position="5"> For use within the semantics module, a preprocessor transforms the dictionary entries to a network representation of concepts.</Paragraph>
    <Paragraph position="6"> The network scheme is influenced by the formalism of Structured Inheritance Networks /1/ and is described in /2/. It is used for knowledge representation in all semantic and pragmatic modules in the system.</Paragraph>
    <Paragraph position="7"> Similar to the frame theoretic approach, the underlying assumption in case theory is that words evoke certain contextual expectations to the hearer, based on his personal experiences and his knowledge on stereotypic situations. In our system, this assumption is adopted in that we use case descriptions not only for verifying syntactic hypotheses, but also for syntactic and semantic predictions about the rest of the sentence. Tiffs top-down aspect plays an essential role not only in the semantic component but in the whole recognition process.</Paragraph>
  </Section>
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