Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM

Lars Hillebrand; David Biesner; Christian Bauckhage; Rafet Sifa

doi:10.1007/978-3-030-57321-8_22

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arXiv · 2507.16695

Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM

Lars Hillebrand·David Biesner·Christian Bauckhage·Rafet Sifa

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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.

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Lars Hillebrand, David Biesner, Christian Bauckhage, Rafet Sifa. 2025-07-22. Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM. https://doi.org/10.1007/978-3-030-57321-8_22

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