Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 103-120).

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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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.

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