TAILIEUCHUNG - Báo cáo khoa học: "Bootstrapping Statistical Parsers from Small Datasets"

We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition, we consider the problem of bootstrapping parsers when the manually parsed training material is in a different domain to either the raw sentences or the testing material. We show that bootstrapping continues to be useful, even though no manually produced parses from the target domain are used. these tasks typically involved a small set. | Bootstrapping Statistical Parsers from Small Datasets Mark Steedman Miles Osborne Anoop Sarkar Stephen Clark Rebecca Hwa Julia Hockenmaier Paul Ruhlen. Steven Baker and Jeremiah Crim Division of Informatics University of Edinburgh steedman stephenc julia osborne @ School of Computing Science Simon Fraser University anoop@cs . sfu . ca Institute for Advanced Computer Studies University of Maryland hwagumiacs . umd. edu Center for Language and Speech Processing Johns Hopkins University jcrimg Department of Computer Science Cornell University sdb2 2 @ Cornell. edu Abstract We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition we consider the problem of bootstrapping parsers when the manually parsed training material is in a different domain to either the raw sentences or the testing material. We show that bootstrapping continues to be useful even though no manually produced parses from the target domain are used. 1 Introduction In this paper we describe how co-training Blum and Mitchell 1998 can be used to bootstrap a pair of statistical parsers from a small amount of annotated training data. Co-training is a weakly supervised learning algorithm in which two or more learners are iteratively retrained on each other s output. It has been applied to problems such as word-sense disambiguation Yarowsky 1995 web-page classification Blum and Mitchell 1998 and named-entity recognition Collins and Singer 1999 . However these tasks typically involved a small set of labels around 2-3 and a relatively small parameter space. It is therefore instructive to consider co-training for more complex models. Compared to these earlier models a statistical parser has a larger parameter space and instead

• Báo cáo khoa học
• Bootstrapping Statistical Parsers
• Small Datasets
• Mark Steedman
• 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
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• UNGRAMHATICALITY
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• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
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• Natural Language Processing
• Mark H
• Bursteln