AutoTM 2.0: Automatic Topic Modeling Framework for Documents Analysis
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910627069427712 |
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| author | Khodorchenko, Maria Butakov, Nikolay Zuev, Maxim Nasonov, Denis |
| author_facet | Khodorchenko, Maria Butakov, Nikolay Zuev, Maxim Nasonov, Denis |
| contents | In this work, we present an AutoTM 2.0 framework for optimizing additively regularized topic models. Comparing to the previous version, this version includes such valuable improvements as novel optimization pipeline, LLM-based quality metrics and distributed mode.
AutoTM 2.0 is a comfort tool for specialists as well as non-specialists to work with text documents to conduct exploratory data analysis or to perform clustering task on interpretable set of features. Quality evaluation is based on specially developed metrics such as coherence and gpt-4-based approaches. Researchers and practitioners can easily integrate new optimization algorithms and adapt novel metrics to enhance modeling quality and extend their experiments.
We show that AutoTM 2.0 achieves better performance compared to the previous AutoTM by providing results on 5 datasets with different features and in two different languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00655 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoTM 2.0: Automatic Topic Modeling Framework for Documents Analysis Khodorchenko, Maria Butakov, Nikolay Zuev, Maxim Nasonov, Denis Machine Learning Computation and Language In this work, we present an AutoTM 2.0 framework for optimizing additively regularized topic models. Comparing to the previous version, this version includes such valuable improvements as novel optimization pipeline, LLM-based quality metrics and distributed mode. AutoTM 2.0 is a comfort tool for specialists as well as non-specialists to work with text documents to conduct exploratory data analysis or to perform clustering task on interpretable set of features. Quality evaluation is based on specially developed metrics such as coherence and gpt-4-based approaches. Researchers and practitioners can easily integrate new optimization algorithms and adapt novel metrics to enhance modeling quality and extend their experiments. We show that AutoTM 2.0 achieves better performance compared to the previous AutoTM by providing results on 5 datasets with different features and in two different languages. |
| title | AutoTM 2.0: Automatic Topic Modeling Framework for Documents Analysis |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2410.00655 |