A corpus-based approach to language learning
A corpus-based approach to language learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Exploring the role of punctuation in parsing natural text
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Comma restoration using constituency information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Using machine learning techniques to build a comma checker for Basque
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Syntactically-informed models for comma prediction
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Restoring punctuation and capitalization in transcribed speech
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal 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
The ups and downs of preposition error detection in ESL writing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A more precise analysis of punctuation for broad-coverage surface realization with CCG
GEAF '08 Proceedings of the Workshop on Grammar Engineering Across Frameworks
Restoring Punctuation and Casing in English Text
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Automated Grammatical Error Detection for Language Learners
Automated Grammatical Error Detection for Language Learners
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
Using parse features for preposition selection and error detection
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Better punctuation prediction with dynamic conditional random fields
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Helping our own: text massaging for computational linguistics as a new shared task
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Grammatical error correction with alternating structure optimization
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Developing methodology for Korean particle error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
High-order sequence modeling for language learner error detection
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
University of Illinois system in HOO text correction shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
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While the field of grammatical error detection has progressed over the past few years, one area of particular difficulty for both native and non-native learners of English, comma placement, has been largely ignored. We present a system for comma error correction in English that achieves an average of 89% precision and 25% recall on two corpora of unedited student essays. This system also achieves state-of-the-art performance in the sister task of restoring commas in well-formed text. For both tasks, we show that the use of novel features which encode long-distance information improves upon the more lexically-driven features used in prior work.