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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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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