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History-Aware Expansion and Fuzzy for Query Reformulation

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Artificial Intelligence (CICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13070))

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Abstract

Query reformulation is the task of rewriting users’ query to predict their information need. A user often struggles to modify a query by adding or removing terms when interacting with search engines. To address this issue, we propose a history-aware expansion and fuzzy model for query reformulation that improves follow-up queries based on successful history click-through logs. A probabilistic model is thus presented to calculate term weight in history and expand meaningful terms or fuzz trivial terms to follow-up query. Experimental results show that reformulated query can improve search engine results on low-frequency and long-tailed queries.

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Acknowledgements

We thank the reviewers for their comments and suggestions. This paper is supported by NSFC (No. 61906018), Huawei Noah’s Ark Lab, MoE-CMCC “Artificial Intelligence” Project (No. MCM20190701), Beijing Natural Science Foundation (Grant No. 4204100), and BUPT Excellent Ph.D. Students Foundation (No. CX2020309).

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Wei Pang

  2. School of Computer Science, Beijing Information Science and Technology University, Beijing, 100101, China

    Ruixue Duan

Authors
  1. Wei Pang
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  2. Ruixue Duan
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Corresponding author

Correspondence to Wei Pang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Yiran Chen

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. University of British Columbia, Vancouver, BC, Canada

    Jane Wang

  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

  6. Xidian University, Xi'an, China

    Weisheng Dong

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Pang, W., Duan, R. (2021). History-Aware Expansion and Fuzzy for Query Reformulation. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13070. Springer, Cham. https://doi.org/10.1007/978-3-030-93049-3_19

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  • DOI: https://doi.org/10.1007/978-3-030-93049-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93048-6

  • Online ISBN: 978-3-030-93049-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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