Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Scaling Up Context-Sensitive Text Correction
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Automatic error detection in the Japanese learners' English spoken data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Detecting errors in English article usage by non-native speakers
Natural Language Engineering
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
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
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Word sense disambiguation with distribution estimation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Web-scale N-gram models for lexical disambiguation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
Using parse features for preposition selection and error detection
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
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
Exploring the data-driven prediction of prepositions in English
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Handling outlandish occurrences: using rules and lexicons for correcting NLP articles
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
University of Illinois system in HOO text correction shared task
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Predicting learner levels for online exercises of Hebrew
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Informing determiner and preposition error correction with word clusters
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
HOO 2012 error recognition and correction shared task: Cambridge University submission report
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Korea University system in 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
NAIST at the HOO 2012 shared task
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Tense and aspect error correction for ESL learners using global context
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Grammar error correction using pseudo-error sentences and domain adaptation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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
Evidence in automatic error correction improves learners' english skill
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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We consider the problem of correcting errors made by English as a Second Language (ESL) writers and address two issues that are essential to making progress in ESL error correction - algorithm selection and model adaptation to the first language of the ESL learner. A variety of learning algorithms have been applied to correct ESL mistakes, but often comparisons were made between incomparable data sets. We conduct an extensive, fair comparison of four popular learning methods for the task, reversing conclusions from earlier evaluations. Our results hold for different training sets, genres, and feature sets. A second key issue in ESL error correction is the adaptation of a model to the first language of the writer. Errors made by non-native speakers exhibit certain regularities and, as we show, models perform much better when they use knowledge about error patterns of the non-native writers. We propose a novel way to adapt a learned algorithm to the first language of the writer that is both cheaper to implement and performs better than other adaptation methods.