Topic modeling in embedding spaces

Adji B. Dieng, Francisco J.R. Ruiz, David M. Blei

Research output: Contribution to journalArticlepeer-review

333 Scopus citations


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. More specifically, the ETM models each word with a categorical distribution whose natural parameter is the inner product between the word’s 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, in terms of both topic quality and predictive performance.

Original languageEnglish (US)
Pages (from-to)439-453
Number of pages15
JournalTransactions of the Association for Computational Linguistics
StatePublished - 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Communication
  • Human-Computer Interaction
  • Linguistics and Language
  • Computer Science Applications
  • Artificial Intelligence


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