Unsupervised query segmentation using clickthrough for information retrieval

  • Authors:

  • Yanen Li;Bo-Jun Paul Hsu;ChengXiang Zhai;Kuansan Wang

  • Affiliations:

  • University of Illinois at Urbana-Champaign, Urbana, IL, USA;Microsoft Research, Redmond, IL, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA;Microsoft Research, Redmond, WA, USA

  • Venue:
  • Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
  • Year:
  • 2011

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Abstract

Query segmentation is an important task toward understanding queries accurately, which is essential for improving search results. Existing segmentation models either use labeled data to predict the segmentation boundaries, for which the training data is expensive to collect, or employ unsupervised strategy based on a large text corpus, which might be inaccurate because of the lack of relevant information. In this paper, we propose a probabilistic model to exploit clickthrough data for query segmentation, where the model parameters are estimated via an efficient EM algorithm. We further study how to properly interpret the segmentation results and utilize them to improve retrieval accuracy. Specifically, we propose an integrated language model based on the standard bigram language model to exploit the probabilistic structure obtained through query segmentation. Experiment results on two datasets show that our segmentation model outperforms existing segmentation models. Furthermore, extensive experiments on a large retrieval dataset reveals that the results of query segmentation can be leveraged to improve retrieval relevance by using the proposed integrated language model.