Automated postediting of documents
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Evaluating performance of grammatical error detection to maximize learning effect
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Algorithm selection and model adaptation for ESL correction tasks
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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As the number of learners of English is constantly growing, automatic error correction of ESL learners' writing is an increasingly active area of research. However, most research has mainly focused on errors concerning articles and prepositions even though tense/aspect errors are also important. One of the main reasons why tense/aspect error correction is difficult is that the choice of tense/aspect is highly dependent on global context. Previous research on grammatical error correction typically uses pointwise prediction that performs classification on each word independently, and thus fails to capture the information of neighboring labels. In order to take global information into account, we regard the task as sequence labeling: each verb phrase in a document is labeled with tense/aspect depending on surrounding labels. Our experiments show that the global context makes a moderate contribution to tense/aspect error correction.