{"id":159879,"date":"2010-08-23T00:00:00","date_gmt":"2010-08-23T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-large-scale-ranker-based-system-for-search-query-spelling-correction\/"},"modified":"2018-10-16T20:00:05","modified_gmt":"2018-10-17T03:00:05","slug":"a-large-scale-ranker-based-system-for-search-query-spelling-correction","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-large-scale-ranker-based-system-for-search-query-spelling-correction\/","title":{"rendered":"A Large Scale Ranker-Based System for Search Query Spelling Correction"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper makes three significant extensions to a noisy channel speller designed for standard writ-ten text to target the challenging domain of search queries. First, the noisy channel model is subsumed by a more general ranker, which allows a variety of features to be easily incorporated. Second, a distributed infrastructure is proposed for training and applying Web scale n-gram language models. Third, a new phrase-based error model is presented. This model places a probability distribution over transformations between multi-word phrases, and is estimated using large amounts of query-correction pairs derived from search logs. Experiments show that each of these extensions leads to significant improvements over the state-of-the-art baseline methods.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper makes three significant extensions to a noisy channel speller designed for standard writ-ten text to target the challenging domain of search queries. First, the noisy channel model is subsumed by a more general ranker, which allows a variety of features to be easily incorporated. 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