Automated postediting of documents
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic error detection in the Japanese learners' English spoken data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Web-based models for natural language processing
ACM Transactions on Speech and Language Processing (TSLP)
Memory-based learning for article generation
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
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
HLT-SRWS '04 Proceedings of the Student Research Workshop at HLT-NAACL 2004
Evaluating performance of grammatical error detection to maximize learning effect
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Detecting article errors based on the mass count distinction
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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This paper proposes a method for detecting determiner errors, which are highly frequent in learner English. To augment conventional methods, the proposed method exploits a strong tendency displayed by learners in determiner usage, i.e., mistakenly omitting determiners most of the time. Its basic idea is simple and applicable to almost any conventional method. This paper combines this idea with countability prediction, which outperforms the conventional methods, achieving an F-measure of 0.613.