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Automated grammatical error detection for language learners
Title
Automated grammatical error detection for language learners [electronic resource] / Claudia Leacock ... [et al.].
ISBN
9781608454716 (electronic bk.)
9781608454709 (pbk.)
Published
San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2010.
Physical Description
1 online resource (ix, 122 p. : ill.) : digital file.
Local Notes
Access is available to the Yale community.
Notes
Part of: Synthesis digital library of engineering and computer science.
Title from PDF t.p. (viewed on June 4, 2010).
Series from website.
Access and use
Access restricted by licensing agreement.
Summary
It has been estimated that over a billion people are using or learning English as a second or foreign language, and the numbers are growing not only for English but for other languages as well. These language learners provide a burgeoning market for tools that help identify and correct learners' writing errors. Unfortunately, the errors targeted by typical commercial proofreading tools do not include those aspects of a second language that are hardest to learn. This volume describes the types of constructions English language learners find most difficult - constructions containing prepositions, articles, and collocations. It provides an overview of the automated approaches that have been developed to identify and correct these and other classes of learner errors in a number of languages. Error annotation and system evaluation are particularly important topics in grammatical error detection because there are no commonly accepted standards. Chapters in the book describe the options available to researchers, recommend best practices for reporting results, and present annotation and evaluation schemes. The final chapters explore recent innovative work that opens new directions for research. It is the authors' hope that this volume will contribute to the growing interest in grammatical error detection by encouraging researchers to take a closer look at the field and its many challenging problems.
Other formats
Also available in print.
Format
Books / Online
Language
English
Added to Catalog
April 25, 2013
Series
Synthesis lectures on human language technologies, # 9
System details note
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Bibliography
Includes bibliographical references (p. 103-120).
Contents
1. Introduction
Working definition of grammatical error
Prominence of research on English language learners
Some terminology
Automated grammatical error detection: NLP and CALL
Intended audience
Outline
2. History of automated grammatical error detection
In the beginning: from pattern matching to parsing
Introduction to data-driven and hybrid approaches
3. Special problems of language learners
Errors made by English language learners
The influence of L1
Challenges for English language learners
The English preposition system
The English article system
English collocations
Summary
4. Language learner data
Learner corpora
Non-English learner corpora
Using artificially created error corpora
Using well-formed corpora
5. Evaluating error detection systems
Evaluation measures
Evaluation using a corpus of correct usage
Evaluation on learner writing
Verifying results on learner writing
Evaluation on fully-annotated learner corpora
Using multiple raters for evaluation
Checklist for consistent reporting of system results
Summary
6. Article and preposition errors
Overview
Articles
Prepositions
Two end-to-end systems: Criterion and MSR ESL Assistant
7. Collocation errors
Defining collocations
Measuring the strength of association between words
Systems for detecting and correcting collocation errors
8. Different approaches for different errors
Detection of ungrammatical sentences
Heuristic rule-based approaches
More complex verb form errors
Spelling errors
Summary
9. Annotating learner errors
Issues with learner error annotation
Number of raters
Annotation scheme
How to correct an error
Annotation approaches
Annotation tools
Annotation schemes
Examples of comprehensive annotation schemes
Example of a targeted annotation scheme
Proposals for efficient annotation
Sampling approach with multiple annotators
Amazon mechanical Turk
Summary
10. New directions
Recent innovations in error detection
Using very large corpora
Using the web
Using the Google N-Gram corpus
Using machine translation to correct errors
Leveraging L1 tendencies with region web counts
Longitudinal studies
11. Conclusion
Bibliography
Authors' biographies.
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