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Journal Article
Topic Modeling in Embedding Spaces
2020
Overview
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
(
), a generative model of documents that marries traditional topic models with word embeddings. More specifically, the
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
, we develop an efficient amortized variational inference algorithm. The
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.
Publisher
MIT Press,MIT Press Journals, The,The MIT Press
Subject
