TAILIEUCHUNG - Báo cáo khoa học: "Measuring Contextual Fitness Using Error Contexts Extracted from the Wikipedia Revision History"

We evaluate measures of contextual fitness on the task of detecting real-word spelling errors. For that purpose, we extract naturally occurring errors and their contexts from the Wikipedia revision history. We show that such natural errors are better suited for evaluation than the previously used artificially created errors. In particular, the precision of statistical methods has been largely over-estimated, while the precision of knowledge-based approaches has been under-estimated. Additionally, we show that knowledge-based approaches can be improved by using semantic relatedness measures that make use of knowledge beyond classical taxonomic relations. Finally, we show that statistical and knowledgebased methods. | Measuring Contextual Fitness Using Error Contexts Extracted from the Wikipedia Revision History Torsten Zesch Ubiquitous Knowledge Processing Lab UKP-DIPF German Institute for Educational Research and Educational Information Frankfurt Ubiquitous Knowledge Processing Lab UKP-TUDA Department of Computer Science Technische Universitat Darmstadt http Abstract We evaluate measures of contextual fitness on the task of detecting real-word spelling errors. For that purpose we extract naturally occurring errors and their contexts from the Wikipedia revision history. We show that such natural errors are better suited for evaluation than the previously used artificially created errors. In particular the precision of statistical methods has been largely over-estimated while the precision of knowledge-based approaches has been under-estimated. Additionally we show that knowledge-based approaches can be improved by using semantic relatedness measures that make use of knowledge beyond classical taxonomic relations. Finally we show that statistical and knowledgebased methods can be combined for increased performance. 1 Introduction Measuring the contextual fitness of a term in its context is a key component in different NLP applications like speech recognition Inkpen and Desilets 2005 optical character recognition Wick et al. 2007 co-reference resolution Bean and Riloff 2004 or malapropism detection Bolshakov and Gelbukh 2003 . The main idea is always to test what fits better into the current context the actual term or a possible replacement that is phonetically structurally or semantically similar. We are going to focus on malapropism detection as it allows evaluating measures of contextual fitness in a more direct way than evaluating in a complex application which always entails influence from other components . the quality of the optical character recognition module Walker et al. 2010 . A malapropism or real-word spelling error occurs when a word is .

• Báo cáo khoa học
• Measuring Contextual Fitness
• Error Contexts Extracted
• the Wikipedia Revision History
• 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
• 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