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 .

• Báo cáo khoa học
• A STRUCTURED REPRESENTATION
• WORD SENSESIR
• SEMANTIC ANALYSIS
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln