Presentation information

Interactive Session

General Session » Interactive Session

[2Xin5] インタラクティブ1

Wed. Jun 9, 2021 5:20 PM - 7:00 PM Room X (Poster room 1)

[2Xin5-09] An Identifiability Analysis of Scientific Research Impacts by Embedding from a Scholarly Dataset

〇Masanao Ochi1, Masanori Shiro2, Jun'ichiro Mori Mori1, Ichiro Sakata1 (1.The University of Tokyo, 2.National Institute of Advanced Industrial Science and Technology,)

Keywords:Scholarly big data, Scientific research impact

It is essential to identify promising research early for a government or company to decide the future research direction. Besides, with the increase in the digital publication of scientific literature and the increasing fragmentation of research, there is a need to develop techniques to predict future research trends automatically.
Previous research on predicting the impact of scientific research has been conducted using specially designed features for each indicator. On the other hand, recent advances in deep learning technology have facilitated integrating different individual models and constructing more general-purpose models.
However, the possibility of using deep learning techniques to predict the impact indicators of scientific research has not been sufficiently investigated. In this paper, we extracted the number of citations after publication, which is one of the typical impact indicators of scientific research, and the corresponding information in the academic literature as a distributed representation. We analyzed the possibility of identifying papers with high impact.

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