Paper: Exploring Distributional Similarity Based Models For Query Spelling Correction

ACL ID P06-1129
Title Exploring Distributional Similarity Based Models For Query Spelling Correction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

A query speller is crucial to search en- gine in improving web search relevance. This paper describes novel methods for use of distributional similarity estimated from query logs in learning improved query spelling correction models. The key to our methods is the property of dis- tributional similarity between two terms: it is high between a frequently occurring misspelling and its correction, and low between two irrelevant terms only with similar spellings. We present two models that are able to take advantage of this property. Experimental results demon- strate that the distributional similarity based models can significantly outper- form their baseline systems in the web query spelling correction task.