TAILIEUCHUNG - Báo cáo khoa học: "Learning Efficient Parsing"

A corpus-based technique is described to improve the efficiency of wide-coverage high-accuracy parsers. By keeping track of the derivation steps which lead to the best parse for a very large collection of sentences, the parser learns which parse steps can be filtered without significant loss in parsing accuracy, but with an important increase in parsing efficiency. An interesting characteristic of our approach is that it is self-learning, in the sense that it uses unannotated corpora. | Learning Efficient Parsing Gertjan van Noord University of Groningen Abstract A corpus-based technique is described to improve the efficiency of wide-coverage high-accuracy parsers. By keeping track of the derivation steps which lead to the best parse for a very large collection of sentences the parser learns which parse steps can be filtered without significant loss in parsing accuracy but with an important increase in parsing efficiency. An interesting characteristic of our approach is that it is self-learning in the sense that it uses unannotated corpora. 1 Introduction We consider wide-coverage high-accuracy parsing systems such as Alpino a parser for Dutch which contains a grammar based on HPSG and a maximum entropy disambiguation component trained on a treebank. Even if such parsing systems now obtain satisfactory accuracy for a variety of text types a drawback concerns the computational properties of such parsers they typically require lots of memory and are often very slow for longer and very ambiguous sentences. We present a very simple fairly general corpus-based method to improve upon the practical efficiency of such parsers. We use the accurate slow parser to parse many unannotated input sentences. For each sentence we keep track of sequences of derivation steps that were required to find the best parse of that sentence . the parse that obtained the best score highest probability according to the parser itself . Given a large set of successful derivation step sequences we experimented with a variety of simple heuristics to filter unpromising derivation steps. A heuristic that works remarkably well simply states that for a new input sentence the parser can only consider derivation step sequences in which any sub-sequence of length N has been observed at least once in the training data. Experimental results are provided for various heuristics and amounts of training data. It is hard to compare fast accurate parsers with slow .

• Báo cáo khoa học
• Learning Efficient Parsing
• Gertjan van Noord
• 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