Context based spelling correction
Information Processing and Management: an International Journal
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Contextual correlates of synonymy
Communications of the ACM
Scaling Up Context-Sensitive Text Correction
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
A spelling correction program based on a noisy channel model
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Correcting real-word spelling errors by restoring lexical cohesion
Natural Language Engineering
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Efficient parsing of highly ambiguous context-free grammars with bit vectors
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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-Sensitive Error Correction: Using Topic Models to Improve OCR
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Using wiktionary for computing semantic relatedness
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Wisdom of crowds versus wisdom of linguists – measuring the semantic relatedness of words
Natural Language Engineering
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
For the sake of simplicity: unsupervised extraction of lexical simplifications from Wikipedia
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
Evaluating models of latent document semantics in the presence of OCR errors
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Wikipedia revision toolkit: efficiently accessing Wikipedia's edit history
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
| Hi-index | 0.00 |
We evaluate measures of contextual fitness on the task of detecting real-word spelling errors. For that purpose, we extract naturally occurring errors and their contexts from the Wikipedia revision history. We show that such natural errors are better suited for evaluation than the previously used artificially created errors. In particular, the precision of statistical methods has been largely over-estimated, while the precision of knowledge-based approaches has been under-estimated. Additionally, we show that knowledge-based approaches can be improved by using semantic relatedness measures that make use of knowledge beyond classical taxonomic relations. Finally, we show that statistical and knowledge-based methods can be combined for increased performance.