Integration of Neural Embeddings and Probabilistic Models in Topic Modeling
Topic modeling, a way to find topics in large volumes of text, has grown with the help of deep learning. This paper presents two novel approaches to topic modeling by integrating embeddings derived from Bert-Topic with the multi-grain clustering topic model (MGCTM). Recognizing the inherent hierarch...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2403904 |