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<Paper uid="W97-0618">
  <Title>A Programmable Multi-Blackboard Architecture for Dialogue Processing Systems</Title>
  <Section position="3" start_page="98" end_page="99" type="intro">
    <SectionTitle>
2 Information-centered
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="98" end_page="98" type="sub_section">
      <SectionTitle>
Representations
2.1 The Type Hierarchy
</SectionTitle>
      <Paragraph position="0"> We use typed feature structures as defined in (Carpenter1992) throughout the entire system as representation formalism. The notion of a type in a feature structure refers to the fact that every feature structure is assigned a type from a type hierarchy.</Paragraph>
      <Paragraph position="1"> Moreover, for every type, a set of appropriate features is specified so that type inference is possible.</Paragraph>
      <Paragraph position="2"> In our applications, we primarily encode the domain knowledge in the type hierarchy.</Paragraph>
      <Paragraph position="3"> According to Carpenter (Carpenter1992), the type hierarchy of the respective domain is given by a set Type of types and the ordering relation between types E, the subsumption relation. Additionally, we describe which features from a set Feat a type may consist of by so-called appropriateness conditions (Carpenter1992). The type hierarchy allows us to express the IS-A relations (in the following noted in cursive letters) and IS-PART-OF relations (noted in capital letters) that hold between objects. Figure  priateness conditions used in the map application.</Paragraph>
      <Paragraph position="4"> The least specific type is at the bottom of the tree.</Paragraph>
      <Paragraph position="5"> The information in the type hierarchy not only provides the types for the feature structures and defines the relations between them but serves also to restrict variable Substitutions in the rules described below.</Paragraph>
    </Section>
    <Section position="2" start_page="98" end_page="98" type="sub_section">
      <SectionTitle>
2.2 The Semantic Representations
</SectionTitle>
      <Paragraph position="0"> Oftentimes, requests formulated in natural language encode only partial information or are ambiguous.</Paragraph>
      <Paragraph position="1"> The representations of a natural language processing system have to account for this fact. Naturally, feature structures are well-suited for representing paxtial information. However, they do not adequately represent ambiguity. For this reason, underspecified .feature structures have been developed. As feature structures, underspecified feature structures can encode partial information. In addition to feature structures, they are able to leave disjunctions unresolved. Figure 2 shows examples for a typed feature structure and an underspecified feature structure.</Paragraph>
      <Paragraph position="2">  (a) representing the semantics of the noun phrase museums in pittsburgh and an underspecified feature structure (b) representing objects that are compatible with (a).</Paragraph>
      <Paragraph position="3"> In the attribute-value-matrix notation that we use to display underspecified feature structures, the type marked with an asterisk is the most specific lower bound of the types in its scope. The scope is indicated by curly brackets. The alternatives are represented inside curly brackets. Indices behind types identify the typed feature structure to which this information belongs. If there are no indices, the information belongs to all feature structures. Features that are common to only a subset of all represented feature structures are in the scope of the most specific type that is in common to that subset.</Paragraph>
      <Paragraph position="4"> Underspecified feature structures represent sets of feature structures efficiently in that they express both the information that is common to and the information that differs between the feature structures in question. The fact that underspecified feature structures represent informational differences is used when generating clarification questions to generate uniquely referring NPs.</Paragraph>
    </Section>
    <Section position="3" start_page="98" end_page="99" type="sub_section">
      <SectionTitle>
2.3 Generating Noun Phrases and
Clarification Questions
</SectionTitle>
      <Paragraph position="0"> Noun phrases containing descriptions of objects are generated by traversing the feature structure representing the object in depth-first order and mapping the features and types to strings. Since underspecifled feature structures represent unresolved disjunctions, they are an adequate point of departure for generating clarification questions. Underspecified feature structures exPlicitly represent the differences  between feature structures. To generate a clarification question to disambiguate an underspecified feature structure, a noun phrase for every disjunct is generated. The information in the noun phrase must be specific enough to reduce ambiguity in the underspecified structure. The noun phrases are then filled into a template of the form 0o you mean &lt;npl) ..... &lt;nP~_1&gt; or &lt;~Pn&gt;? The following example shows the information used for generating two clarification questions to disambiguate the structure shown in figure 2.</Paragraph>
      <Paragraph position="1">  Example 1 Example 2 Disjunct 1 \[NAME\] I ADDR I STI:tEETNAME \] tADOR I STRZETNUM \] Disjunct 2 \[NAMEJ IADDR \[ STREETNAME \] Disjunct 3 \[NAME\] ~ADDK I STREETNAME \] tADOR I STREETNUM \] (a)  (1) Do you mean carnegie museum of natural history, andy warhol museum or fort pitt museum? (2) Do you mean the one at 4400 forbes ave, the one at sandusky st or the one at 100 forbes ave? (b)  paths shown in (a) single out the information that is sufficient to completely disambiguate the under-specified feature structure shown in figure 2 for any of the three disjuncts. The paths and their values are mapped to strings that are filled into a template to produce the questions shown in (b)</Paragraph>
    </Section>
  </Section>
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