Topic Modeling in Embedding Spaces

@article{Dieng2019TopicMI,
  title={Topic Modeling in Embedding Spaces},
  author={Adji B. Dieng and Francisco J. R. Ruiz and D. Blei},
  journal={Transactions of the Association for Computational Linguistics},
  year={2019},
  volume={8},
  pages={439-453}
}
  • Adji B. Dieng, Francisco J. R. Ruiz, D. Blei
  • Published 2019
  • Computer Science, Mathematics
  • Transactions of the Association for Computational Linguistics
  • 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… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 61 REFERENCES
    Nonparametric Spherical Topic Modeling with Word Embeddings
    • 49
    • PDF
    A Correlated Topic Model Using Word Embeddings
    • 45
    • PDF
    Incorporating Word Correlation Knowledge into Topic Modeling
    • 66
    • PDF
    Generative Topic Embedding: a Continuous Representation of Documents
    • 71
    • PDF
    Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
    • 84
    • PDF
    A Word Embeddings Informed Focused Topic Model
    • 19
    • PDF
    Jointly Learning Word Embeddings and Latent Topics
    • 43
    • PDF
    Gaussian LDA for Topic Models with Word Embeddings
    • 205
    • Highly Influential
    • PDF
    A Neural Framework for Generalized Topic Models
    • 18
    • PDF