| Bin Tan|
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|Authorship||Publications (1), datasets (0), tools (0)|
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|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Unsupervised query segmentation using generative language models and Wikipedia||English||2008||In this paper, we propose a novel unsupervised approach to query segmentation, an important task in Web search. We use a generative query model to recover a query's underlying concepts that compose its original segmented form. The model's parameters are estimated using an expectation-maximization (EM) algorithm, optimizing the minimum description length objective function on a partial corpus that is specific to the query. To augment this unsupervised learning, we incorporate evidence from Wikipedia.||0||0|