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Communication Dans Un Congrès Année : 2019

Proposal for Automatic Extraction Framework of Superconductors Related Information from Scientific Literature

Résumé

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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Dates et versions

hal-02870896 , version 1 (17-06-2020)

Identifiants

  • HAL Id : hal-02870896 , version 1

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Luca Foppiano, Thaer M Dieb, Akira Suzuki, Masashi Ishii. Proposal for Automatic Extraction Framework of Superconductors Related Information from Scientific Literature. 電子情報通信学会サービスコンピューティング研究会 2019年度第一回研究会、 第43回MaDIS研究交流会合同研究会, May 2019, Tsukuba, Japan. ⟨hal-02870896⟩
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