Predictable Meaning Shift: Some Linguistic Properties of Lexical Implication Rules
Proceedings of the First SIGLEX Workshop on Lexical Semantics and Knowledge Representation
An unsupervised method for detecting grammatical errors
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Recognizing syntactic errors in the writing of second language learners
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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
HLT-SRWS '04 Proceedings of the Student Research Workshop at HLT-NAACL 2004
A Method for Recognizing Noisy Romanized Japanese Words in Learner English
IEICE - Transactions on Information and Systems
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
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
Evaluating performance of grammatical error detection to maximize learning effect
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Syntax-driven machine translation as a model of ESL revision
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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
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
A meta learning approach to grammatical error correction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - 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 proposes a method for detecting errors in article usage and singular plural usage based on the mass count distinction. First, it learns decision lists from training data generated automatically to distinguish mass and count nouns. Then, in order to improve its performance, it is augmented by feedback that is obtained from the writing of learners. Finally, it detects errors by applying rules to the mass count distinction. Experiments show that it achieves a recall of 0.71 and a precision of 0.72 and outperforms other methods used for comparison when augmented by feedback.