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<Paper uid="E87-1015">
  <Title>MULTILEVEL SEMANTIC ANALYSIS IN AN AU'I~MATIC SPEECH UNDERSTANDING AND DIALOG SYSTEM</Title>
  <Section position="3" start_page="0" end_page="0" type="metho">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> In the speech understanding and dialog system EVAR (Niemann et al. 1985) developed at our institute there are four different modules for understanding an utterance of the user (Brietzmaun 1984): the syntactic analysis, the task-independent semantic analysis, the domain-dependent pcagmatic analysis, and another module for dialog-specific aspects. The semantic module disregards nearly all of the thematic and situational context. Only isolated utterances are analyzed. So the main points of interests are the semantic consistency of words and the underlying relational structure of the sentence. The analysis of the functional relations is based on the valency and case theory (Tesniero 1966, Fillmore 1967). In this theory the head verb of the sentence determines how many noun groups or prel:csitional groups are needed for building up a syntactically correct and semantically consistent sentence. For these slots in a verb frame further syntactic and semantic restrictions can also be given.</Paragraph>
  </Section>
  <Section position="4" start_page="0" end_page="84" type="metho">
    <SectionTitle>
2. Semtntic and Progmstic Consistency
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="0" end_page="84" type="sub_section">
      <SectionTitle>
Semantic Consistency
</SectionTitle>
      <Paragraph position="0"> The semantic knowledge of the module consists of lexical meanings of words and selectional restrictior~ between them. These restrictions are possible for a special word, fur example the preposition 'nach' ('to Hamburg') requires a noun with the meamng LOCation. In the case of a frame they are for a whole constituent; for example, the verb 'wchnen' ('to live in Hamburg') needs a preposition~l group also with the me'~ning LOCation.</Paragraph>
      <Paragraph position="1"> The selectional restrictions are expressed in the dictionary by the feature SELECTION. The semantic classes (features) are hierarchically organized in a way, so that all subclasses of a class also are accepted as compatible. For example, if a word with the semantic class CONcrete is required, also a word with the class ANimate (a subclass of CONcrete) or with the class HUman (a subclass of ANimate) is accepted.</Paragraph>
      <Paragraph position="2">  In Fig. 1 a part or our semantic classification system for nouns is shown. For each prepo~tiun or adjective there can be determined with which nouns they could be combined. That is done by selecting the semantic class of the head noun of a noun group or prepositional group. For example 'in' in its temporal meaning can be used with nouns as Fig. 2 shows, how this system could be used to solve ambiguities.  For example: coach coach.l.l: .'railway carriage&amp;quot; CLAS~ TRAnsport, LOCation coach.l.2: &amp;quot;privat tutor, trainer in athletics&amp;quot; CLASS: ACtingPerson in in.l.l: &amp;quot;in the evening&amp;quot;  Although there are 4 possibilities for combining the words in their different meanings only one possibility ( in.l.2 I coach.l.l ) is semantic consistent.</Paragraph>
      <Paragraph position="3"> At this time no sooting is provided for 'how compatible' a group of words is, only if it is semantically consistent or not.</Paragraph>
    </Section>
    <Section position="2" start_page="84" end_page="84" type="sub_section">
      <SectionTitle>
Pragmatic Consistency
</SectionTitle>
      <Paragraph position="0"> Because of the above mentioned combinatorial explosion it seems to be useful to integrate also at this task-independent stage of the analysis some domain dependent information.</Paragraph>
      <Paragraph position="1"> This pragmatic inforn~tion should be handled with as few effort as possible. On the other side the effect as a filter should also be as good as possible. What is not intended is to introduce here a first structural analysis but to decide whether a group of words pragmatically fit together or not, only dependent on special features of the words itself.</Paragraph>
      <Paragraph position="2"> For this reason here it is tried to check the pragmatic consistency of groups of words or constituents and give them a pragmatic priority. This priority is not a measure for correctness of the hypothesis, but determines in which order pragmatically checked hypotheses should be further analyzed. It indicates, whether all words of such a group can be interpreted in the same pragmatic concept, and how much the set of possible pragmatic concepts could be restricted.</Paragraph>
      <Paragraph position="3"> In our system the pragmatic (task-specific) knowledge is represented in a semantic network (Brielzmarm 1984) as is the knowledge of the semantic module. The network scheme is influenced by the formalism of Stuetured Inheritance Networks (Brachman 1978). In this pragmatic network at the time six types of information inquiries are modelled. Each of these concepts for an inforrmtion type has as attributes the information that is needed to find an answer for an inquiry of the user. For example, the concept 'timetable information' has an attribute 'From time' which specifies the range of time during which the departure of the train should be (see Fig. 3). This attribute could linguistically be realized for example with the word 'tomorrow'.</Paragraph>
      <Paragraph position="4">  train railroad passenger city time pP(w) ear wagon interval</Paragraph>
      <Paragraph position="6"> For many words in the dictionary a possible set of pragmatic concepts can be determined. With this property of words for each word a pragmatic bitvector pbv(w) is defined. Each bit of such a bitvector represents a concept of the pragmatic network. It therefore has as its length the number of all concepts (at the time 193). In this bitvector a word w has &amp;quot;I&amp;quot; for the following concepts: For concepts that could be realized by the word and all generalizations of that concept.</Paragraph>
      <Paragraph position="7"> For all concepts and their specializations for which the concepts of 1. can be the domain of an attribute.</Paragraph>
      <Paragraph position="8"> If the word belongs to the basic lexicon, i.e. the part of the dictionary that is needed for nearly every domain (for example pronouns or determiners), it gets the &amp;quot;l&amp;quot; with respect to their semantic class. For this there exists a mapping function to pragmatic concepts. For example, all such words which belong to the semantic class TIMe (as 2. to the concept 'time interval' which could be realized by these words.</Paragraph>
      <Paragraph position="9"> In many cases (for example determiners) all bits are setto &amp;quot;l'.</Paragraph>
      <Paragraph position="10"> The pragmatic bitvector of a group of words wl ... wn is then: pbv(wl ... v-n) := pbv(wl) AND pbv(w2) ... AND pbv(wn) The pragmatic priority pP(wl ... wn) is defined as the number of  concept and especially which information type was realized. To make use of contextually determined expectations about the following user utterance the pragmatic interpretation of groups of words can be restricted with: pbv(wl ... wn) AND pbv('timetable information') has to be &gt;0 where pbv('timetable information') is the bitvector for the pragmatic concept 'timetable information' and has the &amp;quot;1&amp;quot; only for the concept itself.</Paragraph>
      <Paragraph position="11"> An example for pragmatic bitvectors and priorities pP(w) is given in</Paragraph>
    </Section>
  </Section>
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