Opportunities and challenges of text mining in materials research

Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications.

Automated summary

Research publications serve as the primary repository of scientific knowledge, but their unstructured format poses obstacles to large-scale analysis. Recent advances in natural language processing have provided tools for information extraction, but challenges arise when applied to scientific text with technical terminology. Text mining methodology in materials science is still in its early stages, with efforts focused on understanding the application of TM and NLP in this field.