Topic Modeling in Embedding Spaces
This addresses the challenge of topic modeling for researchers and practitioners dealing with complex text data, though it is an incremental advancement building on existing techniques.
The paper tackles the problem of learning interpretable topics from documents with large and heavy-tailed vocabularies by developing the Embedded Topic Model (ETM), which combines topic models with word embeddings, resulting in improved topic quality and predictive performance over methods like LDA.
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. In particular, it models each word with a categorical distribution whose natural parameter is the inner product between a word 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 (LDA), in terms of both topic quality and predictive performance.