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Research Papers

Causal knowledge extraction by natural language processing in material science: a case study in chemical vapor deposition

Authors:

Yuya Kajikawa ,

Institute of Engineering Innovation, School of Engineering, the University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8565, Japan, JP
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Yoshihide Sugiyama,

Institute of Engineering Innovation, School of Engineering, the University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8565, Japan, JP
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Hideki Mima,

Institute of Engineering Innovation, School of Engineering, the University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8565, Japan, JP
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Katsumori Matsushima

Institute of Engineering Innovation, School of Engineering, the University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8565, Japan, JP
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Abstract

Scientific publications written in natural language still play a central role as our knowledge source. However, due to the flood of publications, the literature survey process has become a highly time-consuming and tangled process, especially for novices of the discipline. Therefore, tools supporting the literature-survey process may help the individual scientist to explore new useful domains. Natural language processing (NLP) is expected as one of the promising techniques to retrieve, abstract, and extract knowledge. In this contribution, NLP is firstly applied to the literature of chemical vapor deposition (CVD), which is a sub-discipline of materials science and is a complex and interdisciplinary field of research involving chemists, physicists, engineers, and materials scientists. Causal knowledge extraction from the literature is demonstrated using NLP.
DOI: http://doi.org/10.2481/dsj.5.108
How to Cite: Kajikawa, Y. et al., (2006). Causal knowledge extraction by natural language processing in material science: a case study in chemical vapor deposition. Data Science Journal. 5, pp.108–118. DOI: http://doi.org/10.2481/dsj.5.108
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Published on 28 Nov 2006.
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