A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
Scaling Up Context-Sensitive Text Correction
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Contextual spelling correction using latent semantic analysis
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
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
Distributing and porting general linguistic tools
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Correcting real-word spelling errors by restoring lexical cohesion
Natural Language Engineering
Semantic similarity for detecting recognition errors in automatic speech transcripts
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Context-based speech recognition error detection and correction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Computer processing of Arabic script-based languages: current state and future directions
Semitic '04 Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages
Web-scale N-gram models for lexical disambiguation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using the web for language independent spellchecking and autocorrection
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Real-word spelling correction using Google Web IT 3-grams
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Creating robust supervised classifiers via web-scale N-gram data
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Detecting semantic errors in a text is still a challenging area of investigation. A lot of research has been done on lexical and syntactic errors while fewer studies have tackled semantic errors, as they are more difficult to treat. Compared to other languages, Arabic appears to be a special challenge for this problem. Because words are graphically very similar to each other, the risk of getting semantic errors in Arabic texts is bigger. Moreover, there are special cases and unique complexities for this language. This paper deals with the detection of semantic errors in Arabic texts but the approach we have adopted can also be applied for texts in other languages. It combines four contextual methods (using statistics and linguistic information) in order to decide about the semantic validity of a word in a sentence. We chose to implement our approach on a distributed architecture, namely, a Multi Agent System (MAS). The implemented system achieved a precision rate of about 90% and a recall rate of about 83%.