Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913953788985344 |
|---|---|
| author | Hillebrand, Lars Biesner, David Bauckhage, Christian Sifa, Rafet |
| author_facet | Hillebrand, Lars Biesner, David Bauckhage, Christian Sifa, Rafet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM Hillebrand, Lars Biesner, David Bauckhage, Christian Sifa, Rafet Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.16695 |