Target material extraction from literature describing synthesis procedures in the field of inorganic materials science

Abstract

In the inorganic materials field, research has been conducted to extract target materials, which are the synthetic materials claimed in the papers, to focus on synthetic materials and analyze their physical properties. For the extraction, there is a question whether the conventional named entity recognition systems can extract such target materials. In this study, we built a corpus of papers labeled only with the target materials and applied a deep learning modes, which have shown high performance in conventional named entity extraction extraction tasks, to the corpus to evaluate the extraction performance of the target materials. As a result, we found that the performance of the deep learning model in extracting target materials was lower than that reported in other named entity recognition tasks. We attribute this to the fact that the conventional named entity recognition task settings are not suitable for the task of extracting target materials from articles, and we discuss the shortcomings of the existing task settings and ways to improve them.