Methods › Natural Language Processing › Topic Embeddings › lda2vec
lda2vec
Introduced by Christopher E Moody in Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
lda2vec builds representations over both words and documents by mixing word2vec’s skipgram architecture with Dirichlet-optimized sparse topic mixtures.
The Skipgram Negative-Sampling (SGNS) objective of word2vec is modified to utilize document-wide feature vectors while simultaneously learning continuous document weights loading onto topic vectors. The total loss term L is the sum of the Skipgram Negative Sampling Loss (SGNS) Lⁿᵉᵍᵢⱼ with the addition of a Dirichlet-likelihood term over document weights, L_d. The loss is conducted using a context vector, cⱼ , pivot word vector wⱼ, target word vector wᵢ, and negatively-sampled word vector wₗ:
L = Lᵈ + ΣᵢⱼLⁿᵉᵍᵢⱼ
Lⁿᵉᵍᵢⱼ = logσ(cⱼ·wᵢ) + ∑ⁿₗ₌₀σ(-cⱼ·wₗ)
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec 6 May 2016 · 5 repositories · arXiv:1605.02019Syntology ran 0 of 4 samples · 4 unverified
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Topic Models | 1 |
| Word Embeddings | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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