@inproceedings{kuniyoshi-etal-2020-annotating,
title = "Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature",
author = "Kuniyoshi, Fusataka and
Makino, Kohei and
Ozawa, Jun and
Miwa, Makoto",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.239",
pages = "1941--1950",
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.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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%0 Conference Proceedings
%T Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature
%A Kuniyoshi, Fusataka
%A Makino, Kohei
%A Ozawa, Jun
%A Miwa, Makoto
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F kuniyoshi-etal-2020-annotating
%X 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.
%U https://aclanthology.org/2020.lrec-1.239
%P 1941-1950
Markdown (Informal)
[Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature](https://aclanthology.org/2020.lrec-1.239) (Kuniyoshi et al., LREC 2020)
ACL