Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature

Fusataka Kuniyoshi, Kohei Makino, Jun Ozawa, Makoto Miwa

Abstract
The synthesis process is essential for achieving computational experiment design in the field of inorganic materials chemistry. In this work, we present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the synthesis processes buried in the scientific literature. We define the representation of the synthesis processes using flow graphs, and create a corpus from the experimental sections of 243 papers. The automated machine-reading system is developed by a deep learning-based sequence tagger and simple heuristic rule-based relation extractor. Our experimental results demonstrate that the sequence tagger with the optimal setting can detect the entities with a macro-averaged F1 score of 0.826, while the rule-based relation extractor can achieve high performance with a macro-averaged F1 score of 0.887.
Anthology ID:
2020.lrec-1.239
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1941–1950
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.239
DOI:
Bibkey:
Cite (ACL):
Fusataka Kuniyoshi, Kohei Makino, Jun Ozawa, and Makoto Miwa. 2020. Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1941–1950, Marseille, France. European Language Resources Association.
Cite (Informal):
Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature (Kuniyoshi et al., LREC 2020)
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