A Structured Representation of Word-Senses for Semantic Analysis.

A STRUCTURED REPRESENTATION OF WORD-SENSES IrOR SEMANTIC ANALYSIS. Mafia Teresa Pazienza Dipartimento di Informatica c Sistcmistica, Universita' "La Sapienza", Roma Paola Velardi IBM Rome Scientific (]cntcr ABSTRACT A framework for a structured representation of semantic knowledge (e.g. word-senses) has been defined at the IBM Scientific Center of Roma, as part of a project on Italian Text Understanding. This representation, based on the conceptual graphs formalism [SOW84], expresses deep knowledge (pragmatic) on word-senses. The knowledge base data structure is such as to provide easy access by the semantic verification algorithm. This paper discusses some important problem related to the definition of a semantic knowledge base, as depth versus generality, hierarchical ordering of concept types, etc., and describes the solutions adopted within the text understanding project. INTRODUCTION 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. Processing time and robustness dramatically get worse when the system is required to deeply understand texts in unrestricted domains. 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. 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). 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. 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. 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. 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. The system is based on VM/PROLOG and …

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