文章基本信息

标题：Opportunities and challenges of text mining in materials research
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作者：Olga Kononova ; Tanjin He ; Haoyan Huo 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：3
页码：1-20
DOI：10.1016/j.isci.2021.102155
语种：English
出版社：Elsevier
摘要：SummaryResearch 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.Graphical abstractDisplay OmittedData Analysis; Computing Methodology; Computational Materials Science; Materials Design