期刊
ISCIENCE
卷 24, 期 3, 页码 -出版社
CELL PRESS
DOI: 10.1016/j.isci.2021.102155
关键词
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资金
- National Science Foundation [1922311, 1922372, 1922090]
- Office of Naval Research (ONR) [N00014-16-1-2432]
- U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division [DE-AC02-05-CH11231]
- Assistant Secretary of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, U.S. Department of Energy [DE-AC02-05CH11231]
- Energy Biosciences Institute through the EBI-Shell program [PT74140, PT78473]
- Direct For Mathematical & Physical Scien [1922372] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1922311, 1922090] Funding Source: National Science Foundation
- Division Of Materials Research [1922372] Funding Source: National Science Foundation
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.
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.
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