Papers › Topic Modeling in Embedding Spaces
Topic Modeling in Embedding Spaces
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings. In particular, it models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. To fit the ETM, we develop an efficient amortized variational inference algorithm. The ETM discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation (LDA), in terms of both topic quality and predictive performance.
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Code
Syntology Ran 3 of 13 code samples harvested from 3 repositories linked to this paper; 10 have no recorded run. Of those that ran: 2 ran · violated contract; 1 ran · our draft was wrong.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Topic Models | 20NewsGroups | ETM | C_v | 0.51 | #4 of 6 | Archive leaderboard | report |
| Topic Models | AG News | ETM | C_v | 0.41 | #4 of 6 | Archive leaderboard | report |
| Topic Models | AG News | ETM | NPMI | 0.02 | #4 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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