document: Exploring web scale language models for search query processing It has been widely observed that search queries are composed in a very different style from that of the body or the title of a document . Many techniques explicitly accounting for this language style discrepancy have shown promising results for information retrieval , yet a large scale analysis on the extent of the language differences has been lacking . In this paper , we present an extensive study on this issue by examining the language model properties of search queries and the three text streams associated with each web document : the body , the title , and the anchor text . Our information theoretical analysis shows that queries seem to be composed in a way most similar to how authors summarize documents in anchor texts or titles , offering a quantitative explanation to the observations in past work . We apply these web scale n-gram language models to three search query processing ( SQP ) tasks : query spelling correction , query bracketing and long query segmentation . By controlling the size and the order of different language models , we find that the perplexity metric to be a good accuracy indicator for these query processing tasks . We show that using smoothed language models yields significant accuracy gains for query bracketing for instance , compared to using web counts as in the literature . We also demonstrate that applying web-scale language models can have marked accuracy advantage over smaller ones .
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