  As the amount of text data continues to grow, topic modeling is serving an
important role in understanding the content hidden by the overwhelming quantity
of documents. One popular topic modeling approach is non-negative matrix
factorization (NMF), an unsupervised machine learning (ML) method. Recently,
Semantic NMF with automatic model selection (SeNMFk) has been proposed as a
modification to NMF. In addition to heuristically estimating the number of
topics, SeNMFk also incorporates the semantic structure of the text. This is
performed by jointly factorizing the term frequency-inverse document frequency
(TF-IDF) matrix with the co-occurrence/word-context matrix, the values of which
represent the number of times two words co-occur in a predetermined window of
the text. In this paper, we introduce a novel distributed method, SeNMFk-SPLIT,
for semantic topic extraction suitable for large corpora. Contrary to SeNMFk,
our method enables the joint factorization of large documents by decomposing
the word-context and term-document matrices separately. We demonstrate the
capability of SeNMFk-SPLIT by applying it to the entire artificial intelligence
(AI) and ML scientific literature uploaded on arXiv.
