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SciNER: Extracting Named Entities from Scientific Literature [chapter]

Zhi Hong, Roselyne Tchoua, Kyle Chard, Ian Foster
2020 Lecture Notes in Computer Science  
As a first step towards automating this process, we present a generalizable neural network model, SciNER, for recognizing scientific entities in free text.  ...  The automated extraction of claims from scientific papers via computer is difficult due to the ambiguity and variability inherent in natural language.  ...  The SciNER Model We focus on the task of identifying scientific named entities in scientific publications.  ... 
doi:10.1007/978-3-030-50417-5_23 fatcat:a2m452npzvggda7r5xyh76g2vi

Text Mining for Energy Materials

2022 Journal of Research in Science and Engineering  
The scientific and technological progresses of the chemical and materials science disciplines lead to a significant amount of numerical and textual data stored in the published papers and databases, which  ...  The authors acknowledge computational support from NSCCSZ Shenzhen, China.  ...  articles http://chemxseer.ist.psu.edu CHEMDNER A text mining tool for chemical name recognition https://doi.org/10.1186/1758-2946-7-S1-S1 SciNER A text mining tool for extracting named entities from scientific  ... 
doi:10.53469/jrse.2022.04(03).24 fatcat:gtfsq4ithjcefei2masxa7thra

Models and Processes to Extract Drug-like Molecules From Natural Language Text

Zhi Hong, J. Gregory Pauloski, Logan Ward, Kyle Chard, Ben Blaiszik, Ian Foster
2021 Frontiers in Molecular Biosciences  
However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective.  ...  A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research.  ...  “SciNER: Extracting Named Entities from Scientific Literature,” in International Conference on Computational Science (Amsterdam: Springer), 308–321. doi:10.1007/978-3-030-50417-5_23 CrossRef Full Text  ... 
doi:10.3389/fmolb.2021.636077 pmid:34527701 pmcid:PMC8435623 fatcat:px7vzgcu7vgatousxdtnusuktu