tailieunhanh - Báo cáo khoa học: "Neural Network Probability Estimation for Broad Coverage Parsing"

We present a neural-network-based statistical parser, trained and tested on the Penn Treebank. The neural network is used to estimate the parameters of a generative model of left-corner parsing, and these parameters are used to search for the most probable parse. The parser's performance ( Fmeasure) is within 1% of the best current parsers for this task, despite using a small vocabulary size (512 inputs). | Neural Network Probability Estimation for Broad Coverage Parsing James Henderson Departement d Informatique Univer site de Geneve Abstract We present a neural-network-based statistical parser trained and tested on the Penn Treebank. The neural network is used to estimate the parameters of a generative model of left-comer parsing and these parameters are used to search for the most probable parse. The parser s performance F-measure is within 1 of the best current parsers for this task despite using a small vocabulary size 512 inputs . Crucial to this success is the neural network architecture s ability to induce a finite representation of the unbounded parse history and the biasing of this induction in a linguistically appropriate way. 1 Introduction Many statistical parsers Ratnaparkhi 1999 Collins 1999 Charniak 2001 are based on a history-based probability model Black et al. 1993 where the probability of each decision in a parse is conditioned on the previous decisions in the parse. A major challenge in this approach is choosing a representation of the parse history from which the probability for the next parser decision can be accurately estimated. Previous approaches have used a hand-crafted finite set of features to represent the unbounded parse history Ratna-parkhi 1999 Collins 1999 Charniak 2001 . In the work presented here we automatically induce a finite set of features to represent the unbounded parse history. We perform this induction using an artificial neural network architecture called Simple Synchrony Networks SSNs Lane and Henderson 2001 Henderson 2000 . Because this architecture is specifically designed for processing structures it allows US to impose structurally specified and linguistically appropriate biases on the search for a good history representation. The resulting parser achieves performance far greater than previous approaches to neural network parsing Ho and Chan 1999 Costa et al. 2001 and only marginally

• Báo cáo khoa học
• Neural Network Probability Estimation
• Broad Coverage Parsing
• James Henderson
• 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
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• NATURAL LANGUAGE SYSTEMS
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• 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
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• DATABASES THEORETICAL
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• 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
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• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
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• Douglas E
• Appelt
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• Kathleen R
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• MENTING A DATABASE
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• THE KEY TO THE SELECTION PROBLEM
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• Jeffrey Conklin David D
• McDonald
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• Stuart C
• Shapiro
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• Mark H
• Bursteln