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

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.

Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020), Marseille, France

References in corpus (1)

• Automatically Extracting Action Graphs from Materials Science Synthesis Procedures

Cited by in corpus (3)

• NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature
• Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions -- A Trial Dataset
• Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature