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Refereed Papers

Track: Search: Query Analysis

Paper Title:
Unsupervised Query Segmentation Using Generative Language Models and Wikipedia


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. Experiments show that our approach dramatically improves performance over the traditional approach that is based on mutual information, and produces comparable results with a supervised method. In particular, the basic generative language model contributes a 7.4% improvement over the mutual information based method (measured by segment F1 on the Intersection test set). EM optimization further improves the performance by 14.3%. Additional knowledge from Wikipedia provides another improvement of 24.3%, adding up to a total of 46% improvement (from 0.530 to 0.774).

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