シェン

thumbnail image
シェン
関連ウェブサイト
連絡先

hu.sheng atmark icredd.hokudai.ac.jp

瀧川 一学 グループ
主任研究者
教員
  • thumbnail image
    シェン

研究紹介

研究テーマ

Chemical data structure, representation learning, chemical databases and indexing

キーワード

Databases, data mining, machine learning
研究概要

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.

代表的な研究成果

  • 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