Annotating and Extracting Synthesis Process of All-Solid-State Batteries
from Scientific Literature
- URL: http://arxiv.org/abs/2002.07339v1
- Date: Tue, 18 Feb 2020 02:30:03 GMT
- Title: Annotating and Extracting Synthesis Process of All-Solid-State Batteries
from Scientific Literature
- Authors: Fusataka Kuniyoshi, Kohei Makino, Jun Ozawa, Makoto Miwa
- Abstract summary: We present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system.
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 rule-based relation extractor.
- Score: 10.443499579567069
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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