Funding map using paragraph embedding based on semantic diversity
Takahiro Kawamura (),
Shusaku Egami and
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Takahiro Kawamura: Japan Science and Technology Agency
Katsutaro Watanabe: Japan Science and Technology Agency
Naoya Matsumoto: Japan Science and Technology Agency
Shusaku Egami: Japan Science and Technology Agency
Mari Jibu: Japan Science and Technology Agency
Scientometrics, 2018, vol. 116, issue 2, 941-958
Abstract Maps of science representing the structure of science can help us understand science and technology (S&T) development. Studies have thus developed techniques for analyzing research activities’ relationships; however, ongoing research projects and recently published papers have difficulty in applying inter-citation and co-citation analysis. Therefore, in order to characterize what is currently being attempted in the scientific landscape, this paper proposes a new content-based method of locating research projects in a multi-dimensional space using the recent word/paragraph embedding techniques. Specifically, for addressing an unclustered problem associated with the original paragraph vectors, we introduce paragraph vectors based on the information entropies of concepts in an S&T thesaurus. The experimental results show that the proposed method successfully formed a clustered map from 25,607 project descriptions of the 7th Framework Programme of EU from 2006 to 2016 and 34,192 project descriptions of the National Science Foundation from 2012 to 2016.
Keywords: Paragraph embedding; Thesaurus; Information entropy; Map of science (search for similar items in EconPapers)
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