Kohei Makino


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A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction
Kohei Makino | Makoto Miwa | Yutaka Sasaki
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature
Fusataka Kuniyoshi | Kohei Makino | Jun Ozawa | Makoto Miwa
Proceedings of the Twelfth Language Resources and Evaluation Conference

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.