TAILIEUCHUNG - Báo cáo khoa học: "A STRUCTURED REPRESENTATION OF WORD-SENSESIR OR SEMANTIC ANALYSIS"

A framework for a structured representation of semantic knowledge (. 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. . | A STRUCTURED REPRESENTATION OF WORD-SENSES FOR SEMANTIC ANALYSIS. Maria Teresa Pazienza Dipartimento di Informatica e Sistcmistica Universita La Sapienza Roma Paola Velardi IBM Rome Scientific Center ABSTRACT A framework for a structured representation of semantic knowledge . 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 RESEARCHER 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 .

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