Natural Language Processing

This chapter provides an overview of the application of natural language processing (NLP) techniques in materials science. It explores the challenges and opportunities in information extraction from both text and tables in materials science literature. The chapter highlights the significance of materials-domain language modelsModels, such as MatSciBERT, in improving topic classification, relation classification, and question answering in materials science texts. Additionally, it discusses the use of graph neural networks (GNNs) in extracting material compositions from tables. The chapter concludes by emphasizing the potential of NLP techniques to enhance materials science research and presents future outlooks for advancements in the field. Overall, this chapter showcases the valuable role of NLP in unlocking the wealth of information embedded in materials science literature.