Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science
Abstract
This work studies publications in the field of cognitive science and utilizes mathematical techniques to connect the analysis of the papers' content (abstracts) to the context (citation, journals). We apply hierarchical topic modeling on the abstracts and community detection algorithms on the citation network, and measure content-context discrepancy to find academic fields that study similar topics but do not cite each other or publish in the same venues. These results show a promising, systemic framework to identify opportunities for scientific collaboration in highly interdisciplinary fields such as cognitive science and machine learning.
Cite
Text
Cheng et al. "Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science." NeurIPS 2021 Workshops: AI4Science, 2021.Markdown
[Cheng et al. "Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science." NeurIPS 2021 Workshops: AI4Science, 2021.](https://mlanthology.org/neuripsw/2021/cheng2021neuripsw-joint/)BibTeX
@inproceedings{cheng2021neuripsw-joint,
title = {{Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science}},
author = {Cheng, Lu and Ganesan, Girish and He, William and Silverston, Daniel and Lee, Harlin and Foster, Jacob Gates},
booktitle = {NeurIPS 2021 Workshops: AI4Science},
year = {2021},
url = {https://mlanthology.org/neuripsw/2021/cheng2021neuripsw-joint/}
}