TAILIEUCHUNG - Báo cáo khoa học: "From detecting errors to automatically correcting them"

Faced with the problem of annotation errors in part-of-speech (POS) annotated corpora, we develop a method for automatically correcting such errors. Building on top of a successful error detection method, we first try correcting a corpus using two off-the-shelf POS taggers, based on the idea that they enforce consistency; with this, we find some improvement. After some discussion of the tagging process, we alter the tagging model to better account for problematic tagging distinctions. This modification results in significantly improved performance, reducing the error rate of the corpus. . | From detecting errors to automatically correcting them Markus Dickinson Department of Linguistics Georgetown University mad87@ Abstract Faced with the problem of annotation errors in part-of-speech POS annotated corpora we develop a method for automatically correcting such errors. Building on top of a successful error detection method we first try correcting a corpus using two off-the-shelf POS taggers based on the idea that they enforce consistency with this we find some improvement. After some discussion of the tagging process we alter the tagging model to better account for problematic tagging distinctions. This modification results in significantly improved performance reducing the error rate of the corpus. 1 Introduction Annotated corpora serve as training material and as gold standard testing material for the development of tools in computational linguistics and as a source of data for theoretical linguists searching for relevant language patterns. However they contain annotation errors and such errors provide unreliable training and evaluation data as has been previously shown see ch. 1 of Dickinson 2005 and references therein . Improving the quality of linguistic annotation where possible is thus a key issue for the use of annotated corpora in computational and theoretical linguistics. Research has gone into automatically detecting annotation errors for part-of-speech annotation van Halteren 2000 Kveton and Oliva 2002 Dickinson and Meurers 2003 yet there has been virtually no work on automatically or semi-automatically correcting such annotation 1Oliva 2001 specifies hand-written rules to detect and Automatic correction can speed up corpus improvement efforts and provide new data for NLP technology training on the corpus. Additionally an investigation into automatic correction forces us to re-evaluate the technology using the corpus providing new insights into such technology. We propose in this paper to automatically correct .

• Báo cáo khoa học
• From detecting errors
• automatically correcting them
• Markus Dickinson
• 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