  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 by ubiquity, with only three topics prevalent in almost 40
percent 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.
