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As the amount of text data continues to grow, topic modeling is serving animportant role in understanding the content hidden by the overwhelming quantityof documents. One popular topic modeling approach is non-negative matrixfactorization (NMF), an unsupervised machine learning (ML) method. Recently,Semantic NMF with automatic model selection (SeNMFk) has been proposed as amodification to NMF. In addition to heuristically estimating the number oftopics, SeNMFk also incorporates the semantic structure of the text. This isperformed by jointly factorizing the term frequency-inverse document frequency(TF-IDF) matrix with the co-occurrence/word-context matrix, the values of whichrepresent the number of times two words co-occur in a predetermined window ofthe 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 decomposingthe word-context and term-document matrices separately. We demonstrate thecapability of SeNMFk-SPLIT by applying it to the entire artificial intelligence(AI) and ML scientific literature uploaded on arXiv.

SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection