Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc. Automatically extracting this information to generate a material science knowledge base is a challenging task. In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder-decoder framework, to jointly extract entities and relations from material science articles as a triplet ($entity1, relation, entity2$). Specifically, we target the battery materials and identify five relations to work on - conductivity, coulombic efficiency, capacity, voltage, and energy. Our proposed approach achieved a much better F1-score (0.771) than a previous attempt using ChemDataExtractor (0.716). The overall graphical framework of MatSciRE is shown in Fig 1. The material information is extracted from material science literature in the form of entity-relation triplets using MatSciRE.

翻译：材料科学文献是各类实体（如材料、成分）及其间多种关系（如电导率、电压等）事实信息的丰富来源。自动抽取这些信息以构建材料科学知识库是一项具有挑战性的任务。本文提出MatSciRE（Material Science Relation Extractor，材料科学关系抽取器），一种基于指针网络的编码器-解码器框架，用于从材料科学文献中联合抽取实体与关系，并以三元组（entity1, relation, entity2）形式呈现。我们聚焦电池材料领域，确定了五种待处理关系——电导率、库仑效率、容量、电压及能量。所提方法取得了显著优于此前基于ChemDataExtractor方案（F1值为0.716）的结果（F1值为0.771）。MatSciRE的整体框架如图1所示。该框架通过MatSciRE以实体-关系三元组形式从材料科学文献中提取材料信息。