Modeling Discriminative Global Inference
ICSC '07 Proceedings of the International Conference on Semantic Computing
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Training paradigms for correcting errors in grammar and usage
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Using mostly native data to correct errors in learners' writing: a meta-classifier approach
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
Generating confusion sets for context-sensitive error correction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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
Helping our own: the HOO 2011 pilot shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Correcting comma errors in learner essays, and restoring commas in newswire text
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
NUS at the HOO 2012 shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
The UI system in the HOO 2012 shared task on error correction
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
A beam-search decoder for grammatical error correction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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In this paper, we describe the University of Illinois system that participated in Helping Our Own (HOO), a shared task in text correction. We target several common errors, such as articles, prepositions, word choice, and punctuation errors, and we describe the approaches taken to address each error type. Our system is based on a combination of classifiers, combined with adaptation techniques for article and preposition detection. We ranked first in all three evaluation metrics (Detection, Recognition and Correction) among six participating teams. We also present type-based scores on preposition and article error correction and demonstrate that our approach achieves best performance in each task.