TAILIEUCHUNG - Báo cáo khoa học: " THE KEY TO THE SELECTION PROBLEM IN NATURAL LANGUAGE GENERATION"

We notion of argue that in domains can be where it a strong can be Mann and Moore [1981], on the other hand, while assembling texts dynamically to suit their audience, do so by "over-generating" the set of facts that will be related, and then passing them all through a special filter, leaving out those that are judged to be already known to the audience and letting through those that are new. McKeown [1981] uses a similar technique -- her generator, like Mann and Moore's, must examine every potentially mentionable object in the domain data base and make. | SALIENCE THE KEY TO THE SELECTION PROBLEM IN NATURAL LANGUAGE GENERATION E. Jeffrey Conklin David D. McDonald Department of Computer and Information Science University of Massachusetts Amherst Massachusetts 01003 USA1 ABSTRACT We argue that in domains where a strong notion of salience can be defined it can be used to provide 1 an elegant solution to the selection problem the problem of how to decide whether a given fact should or should not be mentioned in the text and 2 a simple and direct control framework for the entire deep generation process coordinating proposing planning and realization. Deep generation involves reasoning about conceptual and rhetorical facts as opposed to the narrowly linguistic reasoning that takes place during realization. We report on an empirical study of salience in pictures of natural scenes and its use in a computer program that generates descriptive paragraphs comparable to those produced by people. I. The Selection Problem At the heart of research on natural language generation is the question of how to decide what to say and equally important what not to say. This is the selection problem and it has been approached in various ways in the past Direct translation generators such as Swartout 1981 Clancey to appear avoid the problem by leaving the decision to the original designer of the data structures that serve as the templates to the generator this places the burden on that designer to correctly anticipate what degree of detail and presupposed knowledge will be appropriate to a specific audience since on-line adjustments are not possible. 1. This report describes work done in the Department of Computer and Information Science at the University of Massachusetts. It was supported in part by National Science Foundation grant IST 8104984 Michael Arbib and David McDonald Co-Principal Investigators . Mann and Moore 1981 on the other hand while assembling texts dynamically to suit their audience do so by over-generating the set of .

• Báo cáo khoa học
• THE KEY TO THE SELECTION PROBLEM
• NATURAL LANGUAGE GENERATION
• E
• Jeffrey Conklin David D
• McDonald
• 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
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• 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