Inference

Latent Dirichlet allocation

Categorical variable

Machine learning

Philosophy

Artificial intelligence

Generative model

Topic model

Natural language processing

Information retrieval

Word embedding

Computer science

Linguistics

Generative grammar

Embedding

Word (group theory)

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 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.

Topic Modeling in Embedding Spaces