DiffETM: Diffusion Process Enhanced Embedded Topic Model
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912174865121280 |
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| author | Shao, Wei Liu, Mingyang Song, Linqi |
| author_facet | Shao, Wei Liu, Mingyang Song, Linqi |
| contents | The embedded topic model (ETM) is a widely used approach that assumes the sampled document-topic distribution conforms to the logistic normal distribution for easier optimization. However, this assumption oversimplifies the real document-topic distribution, limiting the model's performance. In response, we propose a novel method that introduces the diffusion process into the sampling process of document-topic distribution to overcome this limitation and maintain an easy optimization process. We validate our method through extensive experiments on two mainstream datasets, proving its effectiveness in improving topic modeling performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00862 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffETM: Diffusion Process Enhanced Embedded Topic Model Shao, Wei Liu, Mingyang Song, Linqi Computation and Language Artificial Intelligence Information Retrieval Machine Learning The embedded topic model (ETM) is a widely used approach that assumes the sampled document-topic distribution conforms to the logistic normal distribution for easier optimization. However, this assumption oversimplifies the real document-topic distribution, limiting the model's performance. In response, we propose a novel method that introduces the diffusion process into the sampling process of document-topic distribution to overcome this limitation and maintain an easy optimization process. We validate our method through extensive experiments on two mainstream datasets, proving its effectiveness in improving topic modeling performance. |
| title | DiffETM: Diffusion Process Enhanced Embedded Topic Model |
| topic | Computation and Language Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2501.00862 |