TAILIEUCHUNG - Báo cáo khoa học: "UNDERSTANDING SCENE DESCRIPTIONS AS EVg~NT SIMULATIONS "

The language of scene descriptions 2 must allow a hearer to build structures of schemas similar (to some level of detail) to those the speaker has built via perceptual processes. The understanding process in general requires a hearer to create and run "event ~ " to check the consistency and plausibility of a "picture" constructed from a speaker's description. A speaker must also run similar event simulations on his own descriptions in order to be able to judge when the hearer has been given sufficient information to construct an appropriate "picture", and to be able to respond appropriately to. | UNDERSTANDING SCENE DESCRIPTIONS AS EVENT SIMULATIONS1 David L. Waltz University of Illinois at Urbana-Champaign 2 The language of scene descriptions must allow a hearer to build structures of schemas similar to some level of detail to those the speaker has built via perceptual processes. The understanding process In general requires a hearer to create and run event simulations1 to check the consistency and plausibility of a picture constructed from a speaker s description. A speaker must also run similar event simulations on his own descriptions in order to be able to judge when the hearer has been given sufficient information to construct an appropriate picture and to be able to respond appropriately to the hearer 3 questions about or responses to the scene description. In this paper I explore some simple scene description examples in which a hearer must make judgements involving reasoning about scenes space common-sense physics cause-effect relationships etc. While I propose some mechanisms for dealing with such scene descriptions my primary concern at this time is to flesh out our understanding of just what the mechanisms must accomplish what information will be available to them and what information must be found or generated to account for the inferences we know are actually made. 1. THE PROBLEM AREA An entity human or computer that could be said to fully understand scene descriptions would have to have a broad range of abilities. For example it would have to be able to make predictions about likely futures to judge certain scene descriptions to be implausible or impossible to point to Items In a scene given a description of the scene and to say whether or not a scene description corresponded to a given scene experienced through other sensory In general then the entity would have to have a sensory system that It could use to generate scene representations to be compared with scene representations it had generated on the basis of natural language .

• UNDERSTANDING SCENE
• DESCRIPTIONS AS SIMULATIONS
• I David L
• Waltz
• 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