Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM
arXiv:2507.16695 · doi:10.1007/978-3-030-57321-8_22
Abstract
The DEDICOM algorithm provides a uniquely interpretable matrix factorization method for symmetric and asymmetric square matrices. We employ a new row-stochastic variation of DEDICOM on the pointwise mutual information matrices of text corpora to identify latent topic clusters within the vocabulary and simultaneously learn interpretable word embeddings. We introduce a method to efficiently train a constrained DEDICOM algorithm and a qualitative evaluation of its topic modeling and word embedding performance.
Accepted and published at CD-MAKE 2020, 20 pages, 8 tables, 8 figures