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Answering unique topic queries with dynamic threshold

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Abstract

Queries with threshold are common when dealing with unstructured data such as text corpus. It often requires several exploring attempts for users to achieve final results. In this work, we propose an automatic sampling method for threshold determination without any interaction with users, in which two optimizing algorithms are introduced to reach the lower-bound time complexity in each sampling trial. We evaluate our methods using several experiments and demonstrate the effectiveness of it, which can be an enormously powerful tool for ordinary users.

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Acknowledgment

This work is supported by NSFC(No. 61732004, 61370080) and the Shanghai Innovation Action Project (No. 16DZ1100200).

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Authors and Affiliations

  1. Fudan University, Shanghai, China

    Huixin Ma, Zhihui Yang, Yinan Jing, Zhenying He & X. Sean Wang

  2. Shanghai Key Laboratory of Data Science, Shanghai, China

    Zhenying He & X. Sean Wang

  3. Shanghai Institute of Intelligent Electronics and Systems, Shanghai, China

    X. Sean Wang

Authors
  1. Huixin Ma
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  2. Zhihui Yang
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  3. Yinan Jing
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  4. Zhenying He
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  5. X. Sean Wang
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Corresponding authors

Correspondence to Yinan Jing, Zhenying He or X. Sean Wang.

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Ma, H., Yang, Z., Jing, Y. et al. Answering unique topic queries with dynamic threshold. World Wide Web 22, 39–58 (2019). https://doi.org/10.1007/s11280-018-0528-7

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