tailieunhanh - Báo cáo khoa học: "Encoding and Acquiring Meanings for-Figurative Phrases *"

Here we address the problem of mapping phrase meanings into their conceptual representations. Figurative phrases are pervasive in human communication, yet they are difficult to explain theoretically. In fact, the ability to handle idiosyncratic behavior of phrases should be a criterion for any theory of lexical representation. Due to the huge number of such phrases in the English language, phrase representation must be amenable to parsing, generation, and also to learning. In this paper we demonstrate a semantic representation which facilitates, for a wide variety of phrases, both learning and parsing. . | Encoding and Acquiring Meanings forTigurative Phrases Michael G. Dyer Uri Zemik Artificial Intelligence Laboratory Computer Science Department 3531 Boelter Hall University of California Los Angeles California 90024 Abstract Here we address the problem of mapping phrase meanings into theứ conceptual representations. Figurative phrases are pervasive in human communication yet they are difficult to explain theoretically. In fact the ability to handle idiosyncratic behavior of phrases should be a criterion for any theory of lexical representation. Due to the huge number of such phrases in the English language phrase representation must be amenable to parsing generation and also to learning. In this paper we demonstrate a semantic representation which facilitates for a wide variety of phrases both learning and parsing. 1. Introduction The phrasal approach to language processing Becker75 Pawley83 Fillmore86 emphasizes the role of the lexicon as a knowledge source. Rather than maintaining a single generic lexical entry for each word . take the lexicon contains many phrases . take over take it or leave it take it up with take it for granted etc. Although this approach proves effective in parsing and in generation Wilensky84 there are three problems which require further investigation. First phrase interaction- the lexicon provides representation for single phrases such as take to task and make up one s mind. Yet it is required to analyze complex clauses such as he made up his mind to take her to task. The problem lies with the way the meanings of the two phrases interact to form the compound meaning. Second phrase ambiguity Zemik86 phrasal parsing shifts the task from single-word selection to the selection of entire lexical phrases. When a set of lexical phrases appear syntactically equivalent . he ran into a friend he ran into an 1986 Mercedes he ran into the store and he ran into trouble again disambiguation must be performed by semantic means. The conditions .

• Báo cáo khoa học
• Encoding and Acquiring Meanings
• Figurative Phrases
• Michael G
• Dyer Uri Zernik
• 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