TAILIEUCHUNG - Báo cáo khoa học: " Automatic Verb Classification Using Distributions of Grammatical Features"

We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diatheses, extracted from a very large annotated corpus. Distributions of four grammatical features are sufficient to reduce error rate by 50% over chance. We conclude that corpus data is a usable repository of verb class information, and that corpus-driven extraction of grammatical features is a promising methodology for automatic lexical acquisition. . | Proceedings of EACL 99 Automatic Verb Classification Using Distributions of Grammatical Features Suzanne Stevenson Paola Merlo Dept of Computer Science and Center for Cognitive Science RuCCS Rutgers University CoRE Building Busch Campus New Brunswick NJ 08903 . LATL-Department of Linguistics University of Geneva 2 rue de Candolle 1211 Geneve 4 Switzerland Abstract We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes based on distributional approximations of diatheses extracted from a very large annotated corpus. Distributions of four grammatical features are sufficient to reduce error rate by 50 over chance. We conclude that corpus data is a usable repository of verb class information and that corpus-driven extraction of grammatical features is a promising methodology for automatic lexical acquisition. 1 Introduction Recent years have witnessed a shift in grammar development methodology from crafting large grammars to annotation of corpora. Correspondingly there has been a change from developing rule-based parsers to developing statistical methods for inducing grammatical knowledge from annotated corpus data. The shift has mostly occurred because building wide-coverage grammars is time-consuming error prone and difficult. The same can be said for crafting the rich lexical representations that are a central component of linguistic knowledge and research in automatic lexical acquisition has sought to address this Dorr and Jones 1996 Dorr 1997 among others . Yet there have been few attempts to learn finegrained lexical classifications from the statistical analysis of distributional data analogously to the induction of syntactic knowledge though see . Brent 1993 Klavans and Chodorow 1992 Resnik 1992 . In this paper we propose such an approach for the automatic classification of verbs into lexical semantic We can express the issues raised by this .

• Báo cáo khoa học
• Automatic Verb Classification
• Distributions of Grammatical Features
• Suzanne Stevenson
• scientific reports
• model language
• process natural language
• A General Feature Space
• Eric Joanis and Suzanne Stevenson
• 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
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• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
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• 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
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• Gary G
• Hendrlx and William H
• Lewis
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• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
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• Barbara J
• Grosz
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• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
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• A LOGIC PROGRAMMING PERSPECTIVE
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• PLANNING NATURAL LANGUAGE
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• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
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• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
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• Mark H
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