Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909595695316992 |
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| author | Fittschen, Elisabeth Xia, Bella Celnik, Leib Dilley, Paul Lippincott, Tom |
| author_facet | Fittschen, Elisabeth Xia, Bella Celnik, Leib Dilley, Paul Lippincott, Tom |
| contents | We measure the effects of several implementation choices for the Dynamic Embedded Topic Model, as applied to five distinct diachronic corpora, with the goal of isolating important decisions for its use and further development. We identify priorities that will maximize utility in applied scholarship, including the practical scalability of vocabulary size to best exploit the strengths of embedded representations, and more flexible modeling of intervals to accommodate the uneven temporal distributions of historical writing. Of similar importance, we find performance is not significantly or consistently affected by several aspects that otherwise limit the model's application or might consume the resources of a grid search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora Fittschen, Elisabeth Xia, Bella Celnik, Leib Dilley, Paul Lippincott, Tom Computation and Language Machine Learning We measure the effects of several implementation choices for the Dynamic Embedded Topic Model, as applied to five distinct diachronic corpora, with the goal of isolating important decisions for its use and further development. We identify priorities that will maximize utility in applied scholarship, including the practical scalability of vocabulary size to best exploit the strengths of embedded representations, and more flexible modeling of intervals to accommodate the uneven temporal distributions of historical writing. Of similar importance, we find performance is not significantly or consistently affected by several aspects that otherwise limit the model's application or might consume the resources of a grid search. |
| title | Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2504.19209 |