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<Paper uid="E87-1040">
  <Title>A STRUCTURED REPRESENTATION OF WORD-SENSES IrOR SEMANTIC ANALYSIS.</Title>
  <Section position="2" start_page="0" end_page="249" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> The main problem encountered in natural language (NL) understanding systems is that of the trade-off between depth and extension of the semantic knowledge base.</Paragraph>
    <Paragraph position="1"> Processing time and robustness dramatically get worse when the system is required to deeply understand texts in unrestricted domains.</Paragraph>
    <Paragraph position="2"> For example, the FRUMP system \[DEJ79\], based on scripts \[SHA77\], analyzes texts in a wide domain by performing a superficial analysis. The idea is to capture only the basic information, much in the same way of a hurried newspaper reader.</Paragraph>
    <Paragraph position="3"> A different approach was adopted in the RESEARCtlER system \[LEB83\], whose objective is to answer detailed questions concerning specific texts. The knowledge domain is based on the description of physical objects (MPs: Memory Pointers), and their mutual relations (RWs: Relation Words).</Paragraph>
    <Paragraph position="4"> A further example is provided by BORIS \[LEH83\], one of the most recent systems in the field of text understanding. BORIS was designed to understand as deeply as possible a limited number of stories. A first prototype of BORIS can successfully answer a variety of questions on divorce stories; an extension to different domains appears however extremely complex without structural changes.</Paragraph>
    <Paragraph position="5"> The current status of the art on knowledge representation and language processing does not offer readily available solutions at this regard. The system presented in this paper does not propose a panacea for semantic knowledge representation, but shows the viability of a deep semaatic approach even in unrestricted domains. The features of the Italian Text Understanding system are summarized as follows: Text analysis is performed in four steps: morphologic, morphosyntactic, syntactic and semantic analysis. At each step the results of the preceding steps are used to restrict Ihe current scope of analysis. Hence for example Ihe semantic analyzer uses the syntactic relations identified by the parser to produce an initial set of possiNe interpretations of the sentence.</Paragraph>
    <Paragraph position="6"> Semantic knowledge is represented in a very detailed form (word_sense pragmatics). Logic is used to implement in a uniform and simple framework the data structure representing semantic knowledge and the programs performing semantic verification.</Paragraph>
    <Paragraph position="7"> For a detailed .vcrview of the project and a description of morphological and syntactical analyses refer to \[ANT87\] In \[VEI,g7\] a texl generation system used for Nt. query answering is also described.</Paragraph>
    <Paragraph position="8"> The system is based on VM/PROLOG and analyzes press_agency releases in the economic domain. Even though the specific application oriented the choice of words to be entered in the semantic data base, no other restrictions where added. Press agency releases do not present any specific morphologic or syntactic simplification in the sentence structure.</Paragraph>
    <Paragraph position="9"> This paper deals with definition of knowledge structures for semantic analysis. Basically, the semantic processor collsi,qs of:  1. a dictionary of word definitions.</Paragraph>
    <Paragraph position="10"> 2. a parsing algorithm.</Paragraph>
    <Paragraph position="11">  We here restrict our attention to the first aspect: the semantic verification algorithm is extensively described in \[PAZ87\] The representation formalism adopted for word definitions is the conceptual graph model \[SOW84\], summarized in ,qectiml 2. According to this model, a piece of meaning (sm~teace or word definition) is represented as a graph of ~ r,m q, t~- a.d conceptual re\[alions  Section 3 states a correspondence between conceptual categories (e.g. concepts and relations) and word-senses. A dictionary of hierarchically structured conceptual relations is derived from an analysis of grammar cases.</Paragraph>
    <Paragraph position="12"> Section 4 deals with concept definitions and type hierarchies. Finally, Section 5 gives some implementation detail.</Paragraph>
    <Paragraph position="13"> The present extention of the knowledge base (about 850 word-sense definitions) is only intended to be an test-bed to demonstrate the validity of the knowledge representation scheme and the semantic analyzer. The contribution of this paper is hence in the field of computer science and his objective is to provide a tool for linguistic experts.</Paragraph>
  </Section>
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