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10.1016/j.isci.2021.102155 http://scihub22266oqcxt.onion/10.1016/j.isci.2021.102155 33665573!7905448!33665573     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       iScience 2021 ; 24 (3): 102155 Nephropedia Template TP {{tp|p=33665573|t=2021. Opportunities and challenges of text mining in aterials research |pdf=|usr=}}{{33665573}} gab.com Text Opportunities and challenges of text mining in aterials research JO: iScience http://www.kidney.de/pget.php?&tw=1&pmid=33665573 #FOAvip 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. Twit Text FOAVip Opportunities and challenges of text mining in aterials research ????iScience http://www.kidney.de/pget.php?&tw=1&pmid=33665573 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33665573 #FOAvip English Wikipedia <ref>{{Cite pmid|33665573}}</ref> Opportunities and challenges of text mining in aterials research #MMPMID33665573 Kononova O; He T; Huo H; Trewartha A; Olivetti EA; Ceder G iScience 2021[Mar]; 24 (3): 102155 PMID33665573show ga 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. äOpportunities and challenges of text mining in aterials research (epmc).pdf DeepDyve Pubget Overpricing