Using the web for language independent spellchecking and autocorrection (2009)

by Ben Hutchinson , Grace Y Chung , Gerard Ellis
Venue:In EMNLP
Citations:10 - 0 self

Active Bibliography

Four types of context for automatic spelling correction – Michael Flor
20 Exploring distributional similarity based models for query spelling correction – Mu Li, Yang Zhang - 2006
Managing Misspelled Queries in IR Applications – Jesús Vilares, Manuel Vilares B, Juan Otero B
Bachelor's thesis on Fuzzy lexical matching – Marc Schoolderman, Supervisor Kees Koster, Second Reader Marc Seutter - 2012
Why Press Backspace? Understanding User Input Behaviors in Chinese Pinyin Input Method – Yabin Zheng, Lixing Xie, Zhiyuan Liu, Maosong Sun, Yang Zhang, Liyun Ru
15 Learning phrase-based spelling error models from clickthrough data – Xu Sun, Daniel Micol, Jianfeng Gao, Chris Quirk - 2010
4 Multi-Level Feature Extraction for Spelling Correction – Johannes Schaback
14 A Large Scale Ranker-Based System for Search Query Spelling Correction – Jianfeng Gao, Xiaolong Li, Daniel Micol, Chris Quirk
Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013) – Pratip Samanta, Bidyut B. Chaudhuri
Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness – Farag Ahmed, Ernesto William, De Luca, Andreas Nürnberger
On using context for automatic correction of – Michael Flor, Yoko Futagi
52 Correcting Real-Word Spelling Errors by Restoring Lexical Cohesion – Graeme Hirst, Alexander Budanitsky - 2001
General Terms – Juan Otero, Jesús Vilares, Manuel Vilares
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence User-Dependent Aspect Model for Collaborative Activity Recognition ∗ – Vincent W. Zheng, Qiang Yang
CHIME: An Efficient Error-Tolerant Chinese Pinyin Input Method – Yabin Zheng, Chen Li, Maosong Sun
22 Learning a spelling error model from search query logs – Farooq Ahmad - 2005
1 CloudSpeller: Spelling Correction for Search Queries by Using a Unified Hidden Markov Model with Web-scale Resources – Yanen Li, Huizhong Duan, Chengxiang Zhai
Spelling Correction as an Iterative Process – That Exploits The
4 More Than Words: Using Token Context to Improve Canonicalization Of Historical German – Bryan Jurish - 2010