Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Automatic collocation suggestion in academic writing
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
The effects of learner errors on the development of a collocation detection tool
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Correcting semantic collocation errors with L1-induced paraphrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Towards advanced collocation error correction in Spanish learner corpora
Language Resources and Evaluation
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One of the most common and persistent error types in second language writing is collocation errors, such as learn knowledge instead of gain or acquire knowledge, or make damage rather than cause damage. In this work-in-progress report, we propose a probabilistic model for suggesting corrections to lexical collocation errors. The probabilistic model incorporates three features: word association strength (MI), semantic similarity (via Word-Net) and the notion of shared collocations (or intercollocability). The results suggest that the combination of all three features outperforms any single feature or any combination of two features.