Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An unsupervised method for detecting grammatical errors
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Using an ontology to determine English countability
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning the countability of English nouns from corpus data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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)
HLT '91 Proceedings of the workshop on Speech and Natural Language
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
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A plethora of methods for learning English countability
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A Statistical Model Based on the Three Head Words for Detecting Article Errors
IEICE - Transactions on Information and Systems
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Correcting ESL errors using phrasal SMT techniques
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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 reinforcing noun countability prediction, which plays a crucial role in demarcating correct determiners in machine translation and error detection. The proposed method reinforces countability prediction by introducing a novel heuristics called one countability per discourse. It claims that when a noun appears more than once in a discourse, all instances will share identical countability. The basic idea of the proposed method is that mispredictions can be corrected by efficiently using one countability per discourse heuristics. Experiments show that the proposed method successfully reinforces countability prediction and outperforms other methods used for comparison. In addition to its performance, it has two advantages over earlier methods: (i) it is applicable to any countability prediction method, and (ii) it requires no human intervention to reinforce countability prediction.