Visualizing Text Structure of Scientific Articles Using AI
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- ONOUE Yosuke
- 日本大学 文理学部 情報科学科
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- BABA Kazutaka
- 株式会社ジェイピー・ドット・コム
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- KOYAMADA Koji
- 京都大学 学術情報メディアセンター
Bibliographic Information
- Other Title
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- 人工知能を用いた科学論文の文章構造可視化
- ジンコウ チノウ オ モチイタ カガク ロンブン ノ ブンショウ コウゾウ カシカ
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Abstract
<p>In recent years, as the number of scientific articles submitted to refereed journals has increased, the burden of peer review of researchers is increasing. The increase in peer review burden has led to delays in the publication of articles and deterioration in the quality of peer-review, and the collapse of the peer-reviewed system that has supported science is also a concern. Therefore, it is necessary to develop support technology for peer review to reduce the burden of researchers. In this research, we consider peer-review support using artificial intelligence technology. We used a Doc2Vec to numerically process the text structure of the scientific articles. We showed the differences in the text structure of the accepted and rejected manuscripts of 591 abstracts submitted to the Journal of Visualization. Furthermore, we developed a classification model of acceptance and rejection of the articles using SVM. We achieved a classification accuracy of 75% only with the abstract of the articles.</p>
Journal
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- Journal of the Visualization Society of Japan
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Journal of the Visualization Society of Japan 38 (151), 23-27, 2018
The Visualization Society of Japan
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Details 詳細情報について
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- CRID
- 1390845702294313984
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- NII Article ID
- 130007722455
- 40021685151
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- NII Book ID
- AN10374478
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- ISSN
- 1884037X
- 09164731
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- NDL BIB ID
- 029271341
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed