TAILIEUCHUNG - Báo cáo khoa học: "QUASI-INDEXICAL REFERENCE IN PROPOSITIONAL SEMANTIC NETWORKS"

We discuss how a deductive question-answering system can represent the beliefs or other cognitive states of users, of other (interacting) systems, and of itself. In particular, we examine the representation of first-person beliefs of others (., the ~ / v . ~ ' ~ representation of a user'A belief that he himself is rich). Such beliefs have as an essential component "'quasi-indexical pronouns" (., 'he himself'), and, hence, require for their analysis a method of representing these pronominal constructions and performing valid inferences with them. . | QUASI-INDEXICAL REFERENCE IN PROPOSITIONAL SEMANTIC NETWORKS William J. Rapaport Department of Philosophy. SUNY Fredonia. Fredonia. NY 14063 Department of Computer Science. SUNY Buffalo. Buffalo. NY 14260 Stuart c. Shapiro Department of Computer Science. SUNY Buffalo. Buffalo. NY 14260 ABSTRACT We discuss how a deductive question-answering system can represent the beliefs or other cognitive states of users of other interacting systems and of itself. In particular we examine the representation of first-person beliefs of others . the system s representation of a user1 s belief that he himself is rich . Such beliefs have as an essential component quasi-indexical pronouns . he himself and. hence require for their analysis a method of representing these pronominal constructions and performing valid inferences with them. The theoretical justification for the approach to be discussed is the representation of nested de dicto beliefs . the system s belief that user-1 believes that system-2 believes that user-2 is rich . We discuss a computer implementation of these representations using the Semantic Network Processing System SNePS and an ATN parser-generator with a question-answering capability. 1. IHIROWGIIfiE Consider a deductive knowledge-representation system whose data base contains information about various people its users other perhaps interacting systems or even itself. In order for the system to learn more about these entities to expand its knowledge base it should contain information about the beliefs or desires wants or other cognitive states of these entities and it should be able to reason about them cf. Moore 1977 Creary 1979 Wilks and Bien 1983. Barnden 1983. and Nilsson 1983 9 . Such a data base constitutes the knowledge more accurately the beliefs of the system about these entities and about their beliefs. Among the interrelated issues in knowledge representation that can be raised in such a context are those of multiple reference and the .

• Báo cáo khoa học
• QUASI INDEXICAL REFERENCE
• PROPOSITIONAL SEMANTIC NETWORKS
• William J
• Rapaport
• 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