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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="5" start_page="84" end_page="89" type="evalu">
    <SectionTitle>
3. Scoring
</SectionTitle>
    <Paragraph position="0"> A nmin problem in reducing the amount of hypotheses for further analysis is to find appropriate scores, so that only the hypotheses that are 'better' than a special given limit have to be regarded further. In the semantic module different types of scores  are used&amp;quot; * Reliability scores from the other modules.</Paragraph>
    <Paragraph position="1"> * A score indicating how much of the speech signal is covered by the hypothesis.</Paragraph>
    <Paragraph position="2"> * The pragmatic priority.</Paragraph>
    <Paragraph position="3"> * A score indicating how many slots of a case frame are filled.  For determining this score a function is used that takes into account that a hypothesis does not become always more probable the more parts of a sentence are realized. Also hypotheses built of only short consitutents (i.e. mostly pronouns or adverbs) are less probable.</Paragraph>
    <Paragraph position="4"> 4, Stages of Semantic Analysis At the present time the semantic analysis has three stages. To demonstrate the analysis here an English example is chosen. It is an invented one for we only analyse Gerrmn spoken speech. In Fig. 5 the result of the syntactic analysis is shown: all constituents that are one upon another are competing with regard to the speech signal. To find sentences covering at least most of the range of the speech signal there can be only combined groups of constituents together that are not competing to each other.</Paragraph>
    <Section position="1" start_page="84" end_page="86" type="sub_section">
      <SectionTitle>
4.1 Local Interpretation of Constituents
</SectionTitle>
      <Paragraph position="0"> A constituent (hypothesized by the syntax module) is checked to see whether the selectional restrictions between all of its words are observed. Only if this is true (i.e. the constituent is semantically consistent), and the constituent is also pragmatically consistent, is it regarded for further semantic analysis.</Paragraph>
      <Paragraph position="1"> Selectional restrictions are defined in the lexicon by the attribute SELECTION. For the local interpretation all selectional restrictiom that are given by some words in a constituent to some others in the same constituent have to be proved. There are especially restrictions given by words of special word classes which can be combined with nouns and can restrict the whole set of nouns to a smaller set by semantic means, i.e. the prepositions (see the exan-~le of Fig. 2), the adjectives or even the numbers. In the above example all constituents with a '~&amp;quot; are rejected.</Paragraph>
      <Paragraph position="2">  z want to {~o I a first class coach what does m durinR a first class coach when I with the next train x a fast station I ,e vo H mbu, l the next train\[ is~ to H_amburs  To give a view about how many syntactic constituents semantically are not correct see Fig. 6. The experiments here shown base on real word hypotheses, but for the syntactic analysis only the best word hypotheses are used (between 35 and 132 for a sentence out of more than 2000), All hypotheses about the really spoken words are added.</Paragraph>
    </Section>
    <Section position="2" start_page="86" end_page="88" type="sub_section">
      <SectionTitle>
4.2 Pre-SC/lectlon of Groups of H~qpothescs
</SectionTitle>
      <Paragraph position="0"> The next step is to build up sentences out of the semantic consistent constituents. This is not done by the syntax module because there exist too many possibilities to combine the syntactic constituents to syntactically correct sentences (there exist nearly no restrictions that are independent of semantic features). On the other hand there is always the difficulty with gain in the speech signal (i.e. not or only with low priority with regard to other hypotheses  leave 1. I obl opt opt opt 2. ) TRAnsport LOCation CONcrete TIHe ~/ NG PNG NG ADVG 4./ case: prep is case: prep is  found but really spoken words). For this reason this analysis is done by the semantic module with additional syntactic knowledge. The analysis is based on the valency and case theory. All verbs, but also some nouns and adjectives are associated with case frames which describe the dependencies between the word itself (i.e. the nucleus of the frame) and the constituents with which it could be combined. Such a case frame describes also the underlying relational structure. The frames are represented in a semantic net (see Brielzmann 1984).</Paragraph>
      <Paragraph position="1">  actant with the functional role INSTRUMENT and two optional actants (GOAL and OBJECT). Beside the actants there exist the adjuncts which could be combined with nearly every verb. In the example there is shown only TIME for that is very important for our application, the information about intercity trains. There are different types of restrictions: I. the information if the actant is obligatory or optional  2. the semantic restriction for the nucleus of the comtituent 3. the (syntactic) type of the constituent 4. these are features that exist especially in German: the case of a  noun group, for prepositional groups a set of prepositions that belong to a certain semantic class or a special preposition. If only I.) and 2.) is used, at least the in Fig. 8 shown sentences could be hypothesized for the example.</Paragraph>
      <Paragraph position="2"> First experiments have shown that it is nearly impossible to use only the network formalism for finding sentences because of the combinatorial explosion. On the other hand the process of instantiation does not cope with the posibility that also the nucleus of a case frame will not be found always. Therefore the pre-selection is added to handle these problems.</Paragraph>
      <Paragraph position="3"> The idea is to seek first for groups of constituents which could establish a sentence. What should be avoided is that the same group of hypotheses is analyzed in several different contexts and that too many combinations have to be checked. So the dictionary is organized in a way that all acrants of all frames with the same serrantic restriction and the same type of constituent are represented as one class. These classes are than grouped together to combinations which can appear together in at least one case frame. Each combination has in addition the information in which case frame it can appear.</Paragraph>
      <Paragraph position="4">  With this last information a found group of words could also be accepted if the nucleus is not found. It is even possible to predict a set of nuclei. These could he used as top-down hypotheses for the syntax module or the word recognition module.</Paragraph>
      <Paragraph position="5"> For example for &amp;quot;to leave&amp;quot;:  These combinations do not say anything about sequential order, for, in German, word-order is relatively free. The last possibility is regarded although such a sentence would he grammatically incomplete (the I~UMENT slot is obligatory) to cope with the fact that not all uttered words are recognized by the word recognition module. To reduce the number of combinations the second combination will be eliminated because the class TRAnsport is a specialization of CONcrete (see Fig. 1) and the combination is then also represented by the last possibility. So there arise ambiguities that have to be solved in the last step of the analysis, the instantiation of frames.</Paragraph>
      <Paragraph position="6"> If this method is applied to a dictionary that cont~in~ all of the words used in the above example the result is the following list of combinations (instead of 14 possibilities, if nothing is drawn  During the first stage of the analysis the serramtic consistent constituents are sorted to the above used classes (see Fig. 9) so that a constituent is attached to all classes with which it is semantically compatible and agrees with respect to the constituent type. So the problem of finding instances for the above combinations reduces to combining each element of the set of hypotheses attached to one class to each element of the set of hypotheses attached to the second class of the combination, and so on. If one combination comprises another, for example (PNG-Lcx:) and (PNG-Loe NG-Con), the earlier result is used (the seek is organized as a tree). Restrictions for combining are given by the fact that two hypotheses cannot he competing with regard to the speech signal and by the fact that the found group of words has to he pragmatically consistent.</Paragraph>
      <Paragraph position="7"> To complete these groups there is also tried to f'md temporal adjuncts to each of them (out of the original group and the so found new groups only the best will be furthermore treated as hypotheses). As temporal adjuncts there will be used all constituents which are compatibal with the semantic class &amp;quot;l'INte and chains of such constituents with length of not more than 3 (for example &amp;quot;tomorrow I morning&amp;quot;, &amp;quot;tomorrow I morning I at 9 o' clock'). Up to now no more inforn'ation is used but in the future there will be a module that chooses only in the dialog context interpretable chains of temporal adjuncts.</Paragraph>
      <Paragraph position="8"> With this second step of semantic analysis in Fig. 8 all sentences but 3, 11 and 18 are hypothesized. 3 and 17 are rejected because the constituent type is not correct, 11 is not pragmatically compatibal. All sententces in Fig. 8 satisfy the semantic restrictions. There have been made also experiments that consider in addition simple rules of word order. They cannot he very specific because in German nearly each word order is allowed, especially in spoken  speech. But nethertheless the experiments so far indicate that about a third of all groups are rejecmd with this criterion (for example the sentence 15 in Fig. 8).</Paragraph>
      <Paragraph position="9"> All found groups of hypotheses get the above mentioned scores and are ordered with regard to it.</Paragraph>
    </Section>
    <Section position="3" start_page="88" end_page="89" type="sub_section">
      <SectionTitle>
Results
</SectionTitle>
      <Paragraph position="0"> The results here presented are based on the following utterances (for the conditions of the experiments see also section 4.1): 246a Welche Verbindung kann ich nelmmn? (Which connection should I choose?) 246b Hat dieser Zug auch einen Speisewagen? (Has this train also a dining-car?) 0250 Ich moechte am Freitng moeglichst frueh in Bonn sein. (I want to be at Bonn on Friday as early as possible.) 5518 Er kostet z.elm Mark. (It costs ten marks).</Paragraph>
      <Paragraph position="1"> 5520 Wit mcechten am Wochenende nach Mainz fahren. (We want to go to Mainz at the weekend.) Fig. 10 shows how many groups Of hypotheses were found dependent on the number of word hypotheses per segment in the speech signal (each segment represents one phon). The experiments here have been made by using as restrictions for the combinations  1. the semantic classes and the type of the constituents (without pbv) 2. the semantic classes, the type of the constituents and pragmatic attributes using the pragmatic bitvectors (with pbv) 3. the same conditions as in 2., but in addition some word order  restrictions are checked (word order).</Paragraph>
      <Paragraph position="2"> The really spoken utterances are always found but in soma cases with a very bad score with respect to competing hypotheses. The main reasons for this result and the often high number of hypotheses are: * The analysis of the time adjuncts is too less restrictive. Therefore in the future there will be only used constituents or chains of constituents that can really be interpreted in the dialog context as a time intervall or a special moment. So hypotheses as 'yesterday I then I tommorow' or 'at nine o' clock I next year' no longer are accepted. The referred tirae should also lie in the near future (because of our application).</Paragraph>
      <Paragraph position="3"> * Anaphora could fill (nearly) each slot in each frame (similar as the constituent 'what' in Fig. 9). On the other hand they are often very short. So they appear in many combinations with other constituents. For an anaphoric constituent must have a referent which it represents (for example the constituent 'it' in 5518 could possibly refer to 'ticket'), such constituents should  obtain the semantic and pragmatic attributes of the possible referents - or, if there are none, should not be regarded for future analysis.</Paragraph>
      <Paragraph position="4"> This method will first reduce the number of hypotheses and second will improve the score of a sentence with anaphoric constituents if it was really spoken (or also if it is well interpretable).</Paragraph>
    </Section>
    <Section position="4" start_page="89" end_page="89" type="sub_section">
      <SectionTitle>
4.3 Structural Interpretation
</SectionTitle>
      <Paragraph position="0"> The last step consists in trying to instantiate the found candidates in the semantic network of the module (Briel2mann 1984 and 1986).</Paragraph>
      <Paragraph position="1"> Here all other selectionfl restrictions (i.e. especially the syntactic ones) are checked and thus the amount of hypotheses can be reduced a little bit more. Also the ambiguities have to be solved (see above).</Paragraph>
      <Paragraph position="2"> As a result there are gained instances of frame concepts which are the input for further domain dependent analysis by the pragmatic module.</Paragraph>
      <Paragraph position="3"> This step (the instantiation) now is in work. All others are runnable.</Paragraph>
    </Section>
  </Section>
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