tailieunhanh - Báo cáo khoa học: "Learning Parse and Translation Decisions From Examples With Rich Context"

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state. | Learning Parse and Translation Decisions From Examples With Rich Context Ulf Hermjakob and Raymond J. Mooney Dept of Computer Sciences University of Texas at Austin Austin TX 78712 USA ulf@ mooney@ Abstract We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context as encoded in features which describe the morphological syntactic semantic and other aspects of a given parse state. 1 Introduction The parsing of unrestricted text with its enormous lexical and structural ambiguity still poses a great challenge in natural language processing. The traditional approach of trying to master the complexity of parse grammars with hand-coded rules turned out to be much more difficult than expected if not impossible. Newer statistical approaches with often only very limited context sensitivity seem to have hit a performance ceiling even when trained on very large corpora. To cope with the complexity of unrestricted text parse rules in any kind of formalism will have to consider a complex context with many different morphological syntactic or semantic features. This can present a significant problem because even linguistically trained natural language developers have great difficulties writing and even more so extending explicit parse grammars covering a wide range of natural language. On the other hand it is much easier for humans to decide how specific sentences should be analyzed. We therefore propose an approach to parsing based on learning from examples with a very strong emphasis on context integrating morphological syntactic semantic and other aspects relevant to making good parse decisions thereby also allowing the parsing to be .

• Báo cáo khoa học
• Learning Parse
• Translation Decisions
• Examples With Rich Context
• scientific reports
• model language
• process natural language
• Trimming CFG Parse Trees
• Sentence Compression
• Machine Learning Approaches
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• A Connectionist Architecture
• Learning to Parse
• Long Papers
• Structural Correspondence Learning
• Parse Disambiguation
• Barbara Plank
• 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