TAILIEUCHUNG - Báo cáo khoa học: "Design and Scruffy Text Implementation"

Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably "neat" form, ., newspaper stories and other edited texts. However, a great deal of natural language texts memos, rough drafts, conversation transcripts~ etc., have features that differ significantly from "neat" texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic constructlon, missing periods, etc. Our solution to these problems is to make use of exoectations, based both on knowledge of surface English and on world knowledge of the situation being described. . | Scruffy Text Understanding Design and Implementation of Tolerant Understanders Richard H. Granger Artificial Intelligence Project Computer Science Department University of California Irvine California 92717 ABSTRACT Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably neat form . newspaper stories and other edited texts. However a great deal of natural language text . memos rough drafts conversation transcripts etc. have features that differ significantly from neat texts posing special problems for readers such as misspelled words missing words poor syntactic construction missing periods etc. Our solution to these problems is to make use of expectations. based both on knowledge of surface English and on world knowledge of the situation being described. These syntactic and semantic expectations can be used to figure out unknown words from context constrain the possible word-senses of words with multiple meanings ambiguity fill in missing words ellipsis and resolve referents anaphora . This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program called NOMAD which understands scruffy texts in the domain of Navy messages. Introduction Consider the following scribbled message left by a computer science professor on a colleague s desk 1 Met w chrmn agreed on changes to prposl nxt mtg 3 Feb. A good deal of informal text such as everyday messages like the one above are very ill-formed grammatically and contain misspellings ad hoc abbreviations and lack of important punctuation such as periods between sentences. Yet people seem to easily understand such messages and in fact most people would probably understand the above message just as readily as they would a more well-formed version I met with the chairman and we agreed on what changes had to be made to the proposal. Our next meeting will be on Feb. 3. No extra information .

• Báo cáo khoa học
• Design and Scruffy Text Implementation
• Richard H
• Granger
• Long Papers
• 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
• 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