A Large Scale Ranker-Based System for Search Query Spelling Correction

This paper makes three significant extensions to a noisy channel speller designed for standard written 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.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Jianfeng Gao, Xiaolong Li, Daniel Micol, Chris Quirk, Xu Sun
• Keywords: COLING 2010, Computational Linguistics, Noisy Channel, Noisy Channel Model, Noisy Channel Speller