HU, Sheng

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HU, Sheng
Related Website

hu.sheng atmark

TAKIGAWA, Ichigaku Group
Principal Investigator
Faculty Members
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    HU, Sheng

About the Research

Research Theme

Chemical data structure, representation learning, chemical databases and indexing


Databases, data mining, machine learning
Research Outline

Before I joined ICReDD, my research focused on developing text indexing and mining techniques on query autocompletion by utilizing large text corpus. Query autocompletion (QAC) is an interactive feature that assists users in formulating queries and saving keystrokes. Due to the convenience it brings to users, it has been adopted in many applications, such as Web search engines, integrated development environments (IDEs), and mobile devices. In my previous research, I studied several fundamental problems of QAC and deliver high quality suggestions in an efficient way.

Representative Research Achievements

  • Autocompletion for Prefix-Abbreviated Input
    S. Hu, C. Xiao, J. Qin, Y. Ishikawa, Q. Ma, SIGMOD, 2019, 211–228
    DOI: 10.1145/3299869.3319858
  • Efficient Query Autocompletion with Edit Distance-based Error Tolerance
    C. Xiao, J. Qin, S. Hu, W. Wang, Y. Ishikawa, K. Tsuda, K. Sadakane, The VLDB Journal, 2019.
    DOI: 10.1007/s00778-019-00595-4
  • Scope-aware Code Completion with Discriminative Modeling
    S. Hu, C. Xiao, Y. Ishikawa, , IPSJ JIP, 2019.
    DOI: 10.2197/ipsjjip.27.469
  • An Efficient Algorithm for Location-Aware Query Autocompletion
    S. Hu, C. Xiao, Y. Ishikawa,  IEICE Trans. on Information and Systems, 2018.
    DOI: 10.1587/transinf.2017EDP7152