Detecting errors in English article usage by non-native speakers
Natural Language Engineering
A feedback-augmented method for detecting errors in the writing of learners of English
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Designing and developing a language environment for second language writers
Computers & Education
A Method for Reinforcing Noun Countability Prediction
IEICE - Transactions on Information and Systems
A Method for Recognizing Noisy Romanized Japanese Words in Learner English
IEICE - Transactions on Information and Systems
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
The ups and downs of preposition error detection in ESL writing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
User input and interactions on Microsoft Research ESL Assistant
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Native judgments of non-native usage: experiments in preposition error detection
HumanJudge '08 Proceedings of the Workshop on Human Judgements in Computational Linguistics
Detection of non-native sentences using machine-translated training data
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Mining sequential patterns and tree patterns to detect erroneous sentences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Recognizing noisy romanized Japanese words in learner English
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Training paradigms for correcting errors in grammar and usage
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Using mostly native data to correct errors in learners' writing: a meta-classifier approach
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Sentence correction incorporating relative position and parse template language models
IEEE Transactions on Audio, Speech, and Language Processing
Annotating ESL errors: challenges and rewards
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Generating confusion sets for context-sensitive error correction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Evaluating performance of grammatical error detection to maximize learning effect
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Lexical normalisation of short text messages: makn sens a #twitter
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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
Algorithm selection and model adaptation for ESL correction tasks
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automated whole sentence grammar correction using a noisy channel model
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
Correcting different types of errors in texts
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Exploiting learners' tendencies for detecting english determiner errors
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Measuring intelligibility of japanese learner english
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Detecting article errors based on the mass count distinction
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
HOO 2012 error recognition and correction shared task: Cambridge University submission report
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Lexical normalization for social media text
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
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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This paper describes a method of detecting grammatical and lexical errors made by Japanese learners of English and other techniques that improve the accuracy of error detection with a limited amount of training data. In this paper, we demonstrate to what extent the proposed methods hold promise by conducting experiments using our learner corpus, which contains information on learners' errors.