A High Speed String Correction Method Using a Hierarchical File
IEEE Transactions on Pattern Analysis and Machine Intelligence
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
The String-to-String Correction Problem
Journal of the ACM (JACM)
Automatic spelling correction in scientific and scholarly text
Communications of the ACM
Computer programs for detecting and correcting spelling errors
Communications of the ACM
A technique for computer detection and correction of spelling errors
Communications of the ACM
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Triphone analysis: a combined method for the correction of orthographical and typographical errors
ANLC '88 Proceedings of the second conference on Applied natural language processing
Detecting and correcting morpho-syntactic errors in real texts
ANLC '92 Proceedings of the third conference on Applied natural language processing
XUXEN: a spelling checker/corrector for basque based on two-level morphology
ANLC '92 Proceedings of the third conference on Applied natural language processing
Contextual spelling correction using latent semantic analysis
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Designing spelling correctors for inflected languages using lexical transducers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Knowledge integration in a robust and efficient morpho-syntactic analyzer for French
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
SEMANET '02 Proceedings of the 2002 workshop on Building and using semantic networks - Volume 11
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This paper presents FipsOrtho, a spell checker targeted at learners of French, and a corpus of learners’ errors which has been gathered to test the system and to get a sample of specific language learners’ errors. Spell checkers are a standard feature of many software products, however they are not designed for specific language learners’ errors. After a brief review of the state of the art, we describe the system’s architecture and interfaces. Then we describe our error typology and detail the techniques used to retrieve words and to order proposals appropriately: alphacode, phoneticization, ad-hoc, capitalization, apostrophe, and word separation error methods. Proposals are sorted by a score depending on the method(s) used to retrieve them, on the expected lexical category, gender, number and person, and on the string proximity with the unknown word. Then the test results are presented: a list of individual words containing errors was submitted to the alphacode and phoneticization methods; a corpus of authentic learners’ errors was gathered and analyzed. Finally we conclude the paper with some limitations of the system and ideas for future research.