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The automatic collection of materials information from research papers using Natural Language Processing (NLP) is highly required for rapid materials development using big data, namely materials informatics (MI). The difficulty of this automatic collection is mainly caused by the variety of expressions in the papers, a robust system with tolerance to such variety is required to be developed. In this paper, we report an ongoing interdisciplinary work to construct a system for automatic collection of superconductor-related information from scientific literature using text mining techniques. We focused on the identification of superconducting material names and their critical temperature (Tc) key property. We discuss the construction of a prototype for extraction and linking using machine learning (ML) techniques for the physical information collection. From the evaluation using 500 sample documents, we define a baseline and a direction for future improvements.

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Keyword: TDM, NLP, Machine Learning, Material Informatics, Superconductors

Date published: 2019-05-31

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Manuscript type: Author's version (Accepted manuscript)

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Updated at: 2022-10-03 01:50:55 +0900

Published on MDR: 2021-08-14 03:55:01 +0900