Abstract
We analyze a corpus consisting of more than 17,000 abstracts in the general field of superconductivity, extracted from the arXiv – an online repository of scientific articles. We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of topics. With SeNMFk, we were able to extract coherent topics validated by human experts. From these topics, a few are relatively general and cover broad concepts, while the majority can be precisely mapped to particular scientific effects or measurement techniques. The topics also differ by ubiquity, with only three topics prevalent in almost 40% of the abstract, while each specific topic tends to dominate a small subset of the abstracts. These results demonstrate the ability of SeNMFk to produce a layered and nuanced analysis of large scientific corpora.
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Notes
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In 2018, the academic journal The Review of Higher Education had to suspend accepting submissions due to a two-year backlog.
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“Scientists are drowning in COVID-19 papers. Can new tools keep them afloat?”, J. Brainard, Science (2020).
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- 5.
The software is publicly available at https://github.com/lanl/pyDNMFk.
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All results are available at https://github.com/vstanev1/-NLP_arxiv_supercon.
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Acknowledgments
This research was funded by DOE National Nuclear Security Administration (NNSA) - Office of Defense Nuclear Nonproliferation R&D (NA-22), and supported by the LANL LDRD grant 20190020DR and DOE BES STTR DE-SC0021599.
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Stanev, V., Skau, E., Takeuchi, I., Alexandrov, B.S. (2022). Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2021. Lecture Notes in Computer Science, vol 13127. Springer, Cham. https://doi.org/10.1007/978-3-030-97549-4_41
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