Automated Grammatical Error Detection for Language Learners
Automated Grammatical Error Detection for Language Learners
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
Helping our own: text massaging for computational linguistics as a new shared task
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
A new dataset and method for automatically grading ESOL texts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Helping our own: the HOO 2011 pilot shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
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
Detection and correction of preposition and determiner errors in English: HOO 2012
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
NUS at the HOO 2012 shared task
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
Precision isn't everything: a hybrid approach to grammatical error detection
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
Korea University system in the HOO 2012 shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
A naive Bayes classifier for automatic correction of preposition and determiner errors in ESL text
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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
Memory-based text correction for preposition and determiner errors
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
HOO 2012 shared task: UKP lab system description
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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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Incorrect usage of prepositions and determiners constitute the most common types of errors made by non-native speakers of English. It is not surprising, then, that there has been a significant amount of work directed towards the automated detection and correction of such errors. However, to date, the use of different data sets and different task definitions has made it difficult to compare work on the topic. This paper reports on the HOO 2012 shared task on error detection and correction in the use of prepositions and determiners, where systems developed by 14 teams from around the world were evaluated on the same previously unseen errorful text.