Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization. (arXiv:2201.00687v1 [cs.DL])

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 specific scientific effects or measurement techniques. The
topics also differ …

analysis arxiv dl factorization literature negative semantic