MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation
  Extraction for Material Science Knowledge-base Construction
Ankan Mullick Akash Ghosh G Sai Chaitanya Samir Ghui Tapas Nayak Seung-Cheol Lee Satadeep Bhattacharjee Pawan Goyal
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.