Distributed representation and one-hot representation fusion with gated network for clinical semantic textual similarity.

Journal: BMC medical informatics and decision making
Published Date: Apr 30, 2020

Abstract

BACKGROUND: Semantic textual similarity (STS) is a fundamental natural language processing (NLP) task which can be widely used in many NLP applications such as Question Answer (QA), Information Retrieval (IR), etc. It is a typical regression problem, and almost all STS systems either use distributed representation or one-hot representation to model sentence pairs.

Authors

  • Ying Xiong
    Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.
  • Shuai Chen
    State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
  • Haoming Qin
    Department of Computer Science, Harbin Institute of Technology, Shenzhen, Guangdong, China.
  • He Cao
    Department of Computer Science, Harbin Institute of Technology, Shenzhen, Guangdong, China.
  • Yedan Shen
    Department of Computer Science, Harbin Institute of Technology, Shenzhen, Guangdong, China.
  • Xiaolong Wang
    Cardiovascular Department, Shuguang Hospital Affiliated to Shanghai University of TCM Shanghai, China.
  • Qingcai Chen
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
  • Jun Yan
    Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
  • Buzhou Tang

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