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

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Includes bibliographical references (pages 123-152).

10. Conclusion -- A. Appendix A. Learner corpora -- Bibliography -- Authors' biographies.

9. Emerging directions -- 9.1 Shared tasks in grammatical error correction -- 9.1.1 The 2011 HOO task -- 9.1.2 The 2012 HOO task -- 9.1.3 The CoNLL 2013 shared task -- 9.1.4 Summary -- 9.2 Machine translation and error correction -- 9.2.1 Noisy channel model -- 9.2.2 Round trip machine translation (RTMT) -- 9.3 Real-time crowdsourcing of grammatical error correction -- 9.4 Does automated error feedback improve writing? --

8. Annotating learner errors -- 8.1 Issues with learner error annotation -- 8.1.1 Number of annotators -- 8.1.2 Annotation schemes -- 8.1.3 How to correct an error -- 8.1.4 Annotation approaches -- 8.1.5 Annotation tools -- 8.2 Annotation schemes -- 8.2.1 Examples of comprehensive annotation schemes -- 8.2.2 Example of a targeted annotation scheme -- 8.3 Proposals for efficient annotation -- 8.3.1 Sampling approach with multiple annotators -- 8.3.2 Crowdsourcing annotations -- 8.3.3 Mining online community-driven revision logs -- 8.4 Summary --

7. Different errors and different approaches -- 7.1 Heuristic rule-based approaches -- 7.1.1 Criterion system -- 7.1.2 ESL assistant -- 7.1.3 Other heuristic rule-based approaches -- 7.2 More complex verb form errors -- 7.3 Spelling errors -- 7.4 Punctuation errors -- 7.5 Detection of ungrammatical sentences -- 7.6 Summary --

6. Collocation errors -- 6.1 Defining collocations -- 6.2 Measuring the strength of association between words -- 6.3 Systems for detecting and correcting collocation errors --

5. Data-driven approaches to articles and prepositions -- 5.1 Extracting features from training data -- 5.2 Types of training data -- 5.2.1 Training on well-formed text -- 5.2.2 Artificial errors -- 5.2.3 Error-annotated learner corpora -- 5.2.4 Comparing training paradigms -- 5.3 Methods -- 5.3.1 Classification -- 5.3.2 N-gram statistics, language models, and web counts -- 5.3.3 Web-based methods -- 5.4 Two end-to-end systems: criterion and MSR ESL assistant -- 5.5 Summary --

4. Evaluating error detection systems -- 4.1 Traditional evaluation measures -- 4.2 Evaluation measures for shared tasks -- 4.3 Evaluation using a corpus of correct usage -- 4.4 Evaluation on learner writing -- 4.4.1 Verifying results on learner writing -- 4.4.2 Evaluation on fully annotated learner corpora -- 4.4.3 Using multiple annotators and crowdsourcing for evaluation -- 4.5 Statistical significance testing -- 4.6 Checklist for consistent reporting of system results -- 4.7 Summary --

3. Special problems of language learners -- 3.1 Errors made by English language learners -- 3.2 The influence of L1 -- 3.3 Challenges for English language learners -- 3.3.1 The English preposition system -- 3.3.2 The English article system -- 3.3.3 English collocations -- 3.4 Summary --

2. Background -- 2.1 In the beginning -- 2.2 Introduction to data-driven and hybrid approaches --

1. Introduction -- 1.1 Introduction to the second edition -- 1.2 New to the second edition -- 1.3 Working definition of grammatical error -- 1.4 Prominence of research on English language learners -- 1.5 Some terminology -- 1.6 Automated grammatical error detection: NLP and CALL -- 1.7 Intended audience -- 1.8 Outline --

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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 continue to 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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