Opportunities and challenges of text mining in materials research
Name
1-s2.0-S2589004221001231-main.pdf
Description
Published version
Size
3.13 MB
Format
Adobe PDF
Checksum (MD5)
1551d7c10198d3d2fdc6602b989db81e
Author(s)
Kononova, Olga
He, Tanjin
Huo, Haoyan
Trewartha, Amalie
Olivetti, Elsa A
Ceder, Gerbrand
Date Issued
2021
Journal
iScience
Publisher
Elsevier BV
Citation
Kononova, Olga, He, Tanjin, Huo, Haoyan, Trewartha, Amalie, Olivetti, Elsa A et al. 2021. "Opportunities and challenges of text mining in materials research." iScience, 24 (3).
Version
Final published version
Abstract
© 2021 The Author(s) 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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licens
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.ISCI.2021.102155
