Automated postediting of documents
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
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
Memory-Based Language Processing (Studies in Natural Language Processing)
Memory-Based Language Processing (Studies in Natural Language Processing)
A classifier-based approach to preposition and determiner error correction in L2 English
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Automatically acquiring models of preposition use
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Helping our own: text massaging for computational linguistics as a new shared task
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
A new dataset and method for automatically grading ESOL texts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Helping our own: the HOO 2011 pilot shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
HOO 2012: a report on the preposition and determiner error correction shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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In this paper we describe the technical implementation of our system that participated in the Helping Our Own 2012 Shared Task (HOO-2012). The system employs a number of preprocessing steps and machine learning classifiers for correction of determiner and preposition errors in non-native English texts. We use maximum entropy classifiers trained on the provided HOO-2012 development data and a large high-quality English text collection. The system proposes a number of highly-probable corrections, which are evaluated by a language model and compared with the original text. A number of deterministic rules are used to increase the precision and recall of the system. Our system is ranked among the three best performing HOO-2012 systems with a precision of 31.15%, recall of 22.08% and F1-score of 25.84% for correction of determiner and preposition errors combined.