数据与计算发展前沿2020，Vol.2Issue(5)：99-109,11.DOI:10.11871/jfdc.issn.2096-742X.2020.05.010
深度学习技术在学科融合研究中的应用
Application of Deep Learning Technology in Discipline Integration Research
刘晓东 1倪浩然2
作者信息
- 1. 中国科学院计算机网络信息中心,信息化战略发展与评估中心,北京 100190
- 2. 华威大学,真实系统数学,英国 考文垂
- 折叠
摘要
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.关键词
文本分类/自然语言处理/卷积神经网络/分类算法Key words
text classification/natural language processing/convolutional neural network/classification algorithm引用本文复制引用
刘晓东,倪浩然..深度学习技术在学科融合研究中的应用[J].数据与计算发展前沿,2020,2(5):99-109,11.
