Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918133550284800 |
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| author | Lim, Matte Yeh, Catherine Wattenberg, Martin Viégas, Fernanda Michalatos, Panagiotis |
| author_facet | Lim, Matte Yeh, Catherine Wattenberg, Martin Viégas, Fernanda Michalatos, Panagiotis |
| contents | Many real-world datasets -- from an artist's body of work to a person's social media history -- exhibit meaningful semantic changes over time that are difficult to capture with existing dimensionality reduction methods. To address this gap, we introduce a visualization technique that combines force-based projection and streaming clustering methods to build a spatial-temporal map of embeddings. Applying this technique, we create Chronotome, a tool for interactively exploring evolving themes in time-based data -- in real time. We demonstrate the utility of our approach through use cases on text and image data, showing how it offers a new lens for understanding the aesthetics and semantics of temporal datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces Lim, Matte Yeh, Catherine Wattenberg, Martin Viégas, Fernanda Michalatos, Panagiotis Human-Computer Interaction Computation and Language Computer Vision and Pattern Recognition Machine Learning Many real-world datasets -- from an artist's body of work to a person's social media history -- exhibit meaningful semantic changes over time that are difficult to capture with existing dimensionality reduction methods. To address this gap, we introduce a visualization technique that combines force-based projection and streaming clustering methods to build a spatial-temporal map of embeddings. Applying this technique, we create Chronotome, a tool for interactively exploring evolving themes in time-based data -- in real time. We demonstrate the utility of our approach through use cases on text and image data, showing how it offers a new lens for understanding the aesthetics and semantics of temporal datasets. |
| title | Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces |
| topic | Human-Computer Interaction Computation and Language Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2509.01051 |