Context based spelling correction
Information Processing and Management: an International Journal
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
An unsupervised method for detecting grammatical errors
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
How to detect grammatical errors in a text without parsing it
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Combining Trigram-based and feature-based methods for context-sensitive spelling correction
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Parsing Ill-Formed Text Using an Error Grammar
Artificial Intelligence Review
Correcting real-word spelling errors by restoring lexical cohesion
Natural Language Engineering
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
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
Semantic text similarity using corpus-based word similarity and string similarity
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Real-word spelling correction using Google web 1Tn-gram data set
Proceedings of the 18th ACM conference on Information and knowledge management
Automatic correction of grammatical errors in non-native english text
Automatic correction of grammatical errors in non-native english text
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This paper proposes an unsupervised approach that automatically detects and corrects a text containing multiple errors of both syntactic and semantic nature. The number of errors that can be corrected is equal to the number of correct words in the text. Error types include, but are not limited to: spelling errors, real-word spelling errors, typographical errors, unwanted words, missing words, prepositional errors, punctuation errors, and many of the grammatical errors (e.g., errors in agreement and verb formation).