Improving NMF clustering by leveraging contextual relationships among words

Non-negative Matrix Factorization (NMF) and its variants have been successfully used for clustering text documents. However, NMF approaches like other models do not explicitly account for the contextual dependencies between words. To remedy this limitation, we draw inspiration from neural word embedding and posit that words that frequently co-occur within the same context (e.g., sentence or document) are likely related to each other in some semantic aspect. We then propose to jointly factorize the document-word and word-word co-occurrence matrices. The decomposition of the latter matrix encourages frequently co-occurring words to have similar latent representations and thereby reflecting the relationships among them. Empirical results, on several real-world datasets, provide strong support for the benefits of our approach. Our main finding is that we can drastically improve the clustering performance of NMF by leveraging the contextual relationships among words explicitly.Published by Elsevier B.V.

Automated summary

Non-negative Matrix Factorization (NMF) and its variants are widely used for clustering text documents. However, these methods do not explicitly consider the contextual dependencies between words. To address this issue, researchers draw inspiration from neural word embedding and propose jointly factorizing the document-word and word-word co-occurrence matrices. Empirical results show that this approach significantly improves the clustering performance of NMF on multiple real-world datasets.