Interactive Topic Models with Optimal Transport
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917708210110464 |
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| author | Dhanania, Garima Mysore, Sheshera Pham, Chau Minh Iyyer, Mohit Zamani, Hamed McCallum, Andrew |
| author_facet | Dhanania, Garima Mysore, Sheshera Pham, Chau Minh Iyyer, Mohit Zamani, Hamed McCallum, Andrew |
| contents | Topic models are widely used to analyze document collections. While they are valuable for discovering latent topics in a corpus when analysts are unfamiliar with the corpus, analysts also commonly start with an understanding of the content present in a corpus. This may be through categories obtained from an initial pass over the corpus or a desire to analyze the corpus through a predefined set of categories derived from a high level theoretical framework (e.g. political ideology). In these scenarios analysts desire a topic modeling approach which incorporates their understanding of the corpus while supporting various forms of interaction with the model. In this work, we present EdTM, as an approach for label name supervised topic modeling. EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities and using optimal transport for making globally coherent topic-assignments. In experiments, we show the efficacy of our framework compared to few-shot LLM classifiers, and topic models based on clustering and LDA. Further, we show EdTM's ability to incorporate various forms of analyst feedback and while remaining robust to noisy analyst inputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19928 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interactive Topic Models with Optimal Transport Dhanania, Garima Mysore, Sheshera Pham, Chau Minh Iyyer, Mohit Zamani, Hamed McCallum, Andrew Computation and Language Human-Computer Interaction Information Retrieval Topic models are widely used to analyze document collections. While they are valuable for discovering latent topics in a corpus when analysts are unfamiliar with the corpus, analysts also commonly start with an understanding of the content present in a corpus. This may be through categories obtained from an initial pass over the corpus or a desire to analyze the corpus through a predefined set of categories derived from a high level theoretical framework (e.g. political ideology). In these scenarios analysts desire a topic modeling approach which incorporates their understanding of the corpus while supporting various forms of interaction with the model. In this work, we present EdTM, as an approach for label name supervised topic modeling. EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities and using optimal transport for making globally coherent topic-assignments. In experiments, we show the efficacy of our framework compared to few-shot LLM classifiers, and topic models based on clustering and LDA. Further, we show EdTM's ability to incorporate various forms of analyst feedback and while remaining robust to noisy analyst inputs. |
| title | Interactive Topic Models with Optimal Transport |
| topic | Computation and Language Human-Computer Interaction Information Retrieval |
| url | https://arxiv.org/abs/2406.19928 |