Grammatical error correction with alternating structure optimization
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Creating a manually error-tagged and shallow-parsed learner corpus
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
They can help: using crowdsourcing to improve the evaluation of grammatical error detection systems
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
The StringNet lexico-grammatical knowledgebase and its applications
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
Developing methodology for Korean particle error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
High-order sequence modeling for language learner error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Correcting comma errors in learner essays, and restoring commas in newswire text
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Better evaluation for grammatical error correction
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Google books n-gram corpus used as a grammar checker
EACL 2012 Proceedings of the Second Workshop on Computational Linguistics and Writing (CLW 2012): Linguistic and Cognitive Aspects of Document Creation and Document Engineering
Exploring grammatical error correction with not-so-crummy machine translation
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
HOO 2012: a report on the preposition and determiner error correction shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
On using context for automatic correction of non-word misspellings in student essays
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
VTEX determiner and preposition correction system for the HOO 2012 shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
HOO 2012 error recognition and correction shared task: Cambridge University submission report
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
The UI system in the HOO 2012 shared task on error correction
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Helping our own: NTHU NLPLAB system description
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
A beam-search decoder for grammatical error correction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Evidence in automatic error correction improves learners' english skill
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Bucking the trend: improved evaluation and annotation practices for ESL error detection systems
Language Resources and Evaluation
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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. Table of Contents: Introduction / History of Automated Grammatical Error Detection / Special Problems of Language Learners / Language Learner Data / Evaluating Error Detection Systems / Article and Preposition Errors / Collocation Errors / Different Approaches for Different Errors / Annotating Learner Errors / New Directions / Conclusion