Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (5): 99-109.doi: 10.11871/jfdc.issn.2096-742X.2020.05.010

• Technology and Applicaton • Previous Articles     Next Articles

Application of Deep Learning Technology in Discipline Integration Research

Liu Xiaodong1,*(),Ni Haoran2()   

  1. 1. Center of Informatization Strategy and Evaluation, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. Mathematics for Real-World Systems CDT, University of Warwick, Coventry CV4 7HP, United Kingdom
  • Received:2020-07-02 Online:2020-10-20 Published:2020-10-30
  • Contact: Liu Xiaodong E-mail:liuxiaodong@cnic.cn;Haoran.ni@warwick.ac.uk

Abstract:

[Objective] We use deep learning models to multi-classify articles and analyze the disciplinary integration situation of the corresponding institutions. [Methods] In this paper, we design a one-versus-rest classification model and applied convolutional neural networks to categorize paper abstracts of 8 different main subjects produced by Chinese Academy of the Sciences. [Results] The results show that the cross-integration of disciplines involved in scientific research becomes a more frequent practice and the integration of academic fields are promoting the number of publications of scientific research papers. [Conclusions] This research can benefit the strategic planning and deployment for scientific research institutions.

Key words: text classification, natural language processing, convolutional neural network, classification algorithm