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<Paper uid="C02-1141">
  <Title>A complete integrated NLG system using AI and NLU tools</Title>
  <Section position="4" start_page="0" end_page="0" type="metho">
    <SectionTitle>
4 Using SDRT for document
</SectionTitle>
    <Paragraph position="0"> structuring In (Danlos et al., 2001) we advocate using sdrt (Segmented Discourse Representation Theory (Asher, 1993; Asher and Lascarides, 1998)) as a discourse framework, since sdrt and drt (Discourse Representation Theory, (Kamp and Reyle, 1993)) are the most popular frameworks for formal and computational semantics. Let us briefly present sdrt.</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
4.1 A brief introduction to SDRT
</SectionTitle>
      <Paragraph position="0"> sdrt, designed first for text understanding, was introduced as an extension ofdrtin order to account for specific properties of discourse structure. sdrt can be viewed as a super-layer on drt whose expressiveness is enhanced by the use of discourse relations. Thus the drt structures (Discourse Representation Structures or drs) are handled as basic discourse units in sdrt.</Paragraph>
      <Paragraph position="1"> drss are &amp;quot;boxed&amp;quot; first order logic formulae. Formally, a drs is a couple of sets &lt;U,Con&gt; . U (the universe) is the set of discourse referents. Con contains the truth conditions representing the meaning of the discourse.</Paragraph>
      <Paragraph position="2"> A sdrs is a pair &lt;U,Con&gt; , see Figure 3. U is a set of labels of drs or sdrs which can be viewed as &amp;quot;speech act discourse referents&amp;quot; (Asher and Lascarides, 1998). Con is a set of conditions on labels of the form: * pi : K, where pi is a label from U and K is a (s)drs * R(pii,pij), where pii and pij are labels and R a discourse relation. Discourse relations are inferred non-monotonically by means of a defeasible glue logic exploiting lexical and world knowledge.</Paragraph>
    </Section>
    <Section position="2" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
4.2 Building a SDRS
</SectionTitle>
      <Paragraph position="0"> Starting from a &amp;quot;message&amp;quot; encoded into a logical form, the document structuring module builds a sdrs. On a first step, the logical form is translated into a drs. In the case of a purely existential formula2, this amounts to putting all the variables into the universe of the drs and splitting the formula into elementary conjoined conditions.</Paragraph>
      <Paragraph position="1"> After this first step, the document structuring task amounts to building a sdrs from a drs and to go on recursively on each embedded (s)drs.</Paragraph>
      <Paragraph position="2"> This process is schematized below.</Paragraph>
      <Paragraph position="4"> Let us first examine the principles governing the splitting of the conditions. All the conditions in the drs have to be expressed in the sdrs. Two cases arise:  consequences: no other element is in charge of expressing condition3.</Paragraph>
      <Paragraph position="5"> To establish discourse relations, the sdrt conditions are reversed. As an illustration, in sdrt for text understanding, there is the Axiom (1) for Narration. This axiom states that if Narration holds between two sdrss pi1 and pi2, then the main event (me) of pi1 happens before the main event of pi2.</Paragraph>
      <Paragraph position="7"> For text generation, this axiom is reversed as shown below (Roussarie, 2000, p. 154): * If k1 and k2 are drss whose main eventualities are not states, * and if the main event of k1 occurs before the main event of k2, * then Narration(pi1,pi2) is valid when pi1 and pi2 respectively label k1 and k2.</Paragraph>
      <Paragraph position="8"> As another example, the condition cause(e1,e2) can be expressed through Result(pi1,pi2) or Explanation(pi2,pi1) when pi1 and pi2 label the sub-drss that contain the descriptions of e1 and e2 respectively.</Paragraph>
      <Paragraph position="9"> Let us now examine how we determine the universes of sub-drss, i.e. discourse referents, while observing two technical constraints, namely: * the arguments of any condition in a sub-drs must appear in the universe of this drs; * the universes of all the sub-drss have to be disjoint. This constraint is the counterpart of the following constraint in understanding: &amp;quot;partial drss introduce new discourse referents&amp;quot; (Asher, 1993, p. 71).</Paragraph>
      <Paragraph position="10"> These two constraints are not independent.</Paragraph>
      <Paragraph position="11"> Assuming that the first constraint is respected, the second one can be respected with the following mechanism: if a variable x already appears in a preceding sub-drs labelled pix, then a new variable y is created in the universe of the current sub-drs labelled piy and the condition y = x is added to the conditions of piy. The discourse referent y will be generated as an anaphora if pix is available to piy (Asher, 1993), otherwise it will be generated as a definite or demonstrative NP.</Paragraph>
      <Paragraph position="12"> A document structuring module la sdrt based on the principles we have just exposed can be used for any generator (whose &amp;quot;message&amp;quot; is first order logic formula). The algorithm and the rules establish discourse relations (obtained by reversing the rules in NLU) are generic. See below an example of sdrs in GePhoX, the sdrs built from Table 5.</Paragraph>
      <Paragraph position="14"> Table 6: sdrs for Euclidian division 5 Using a lexicalized grammar for the tactical component Lexicalized grammars are commonly used in NLU and also in NLG (Stede, 1996). In Danlos (1998; 2000) we propose a lexicalized formalism, called g-tag, for the tactical component of an NLG system. It is modularized into a micro-planner which produces a semantic dependency tree and a surface realizer which produces the text (see Figure 1.2).</Paragraph>
      <Paragraph position="15"> The surface realizer is designed to use the syntactic and lexical information of a lexicalized tag grammar. The tag grammar is extended to handle multi-sentential texts and not only isolated sentences.</Paragraph>
      <Paragraph position="16"> The microplanner is based on a lexicalized conceptual-semantic interface. This interface is made up of concepts; each concept is associated with a lexical database. In our model, a concept is either a term in the TBox or a discourse relation. A lexical database for a given concept records the lexemes lexicalizing it with their argument structure, and the mappings between the conceptual and semantic arguments. The process of generating a semantic dependency tree from a sdrs &lt;U,Con&gt; is recursive: - An element pii in U is generated as a clause if pii labels a drs and recursively as a text (possibly a complex sentence) if pii labels a sdrs.</Paragraph>
      <Paragraph position="17"> - A condition R(pii,pij) in Con is generated as a text &amp;quot;Si. Cue Sj.&amp;quot; or as a complex sentence &amp;quot;Si Cue Sj.&amp;quot;, where Si generates pii, Sj pij, and Cue is a cue phrase which is encoded in the lexical database associated with R (Cue may be empty).</Paragraph>
      <Paragraph position="18"> - A condition pi : K in Con where K is a drs &lt;U,Con&gt; is generated as a clause according to the following constraints (which are the counterparts of constraints in understanding): null * A discourse referent in U is generated as an NP or a tensed verb.</Paragraph>
      <Paragraph position="19"> * Conditions guide lexical choices. Conditions such as x = John correspond to proper nouns. Equality conditions between discourse referents (e.g. x = y) give rise to (pronominal or nominal) anaphora. The other conditions, e.g. prove(e1,x,y), are lexicalized through the lexical data base associated with the concept (prove).</Paragraph>
      <Paragraph position="20"> The surface realizer, based on a tag grammar, is a set of lexical data bases. A data base for a given lexical entry encodes the syntactic structures realizing it with their syntactic arguments. With such a tag grammar and a morphological module, the text is computed in a deterministic way from the semantic dependency tree.</Paragraph>
    </Section>
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