期刊
TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
卷 8, 期 -, 页码 439-453出版社
MIT PRESS
DOI: 10.1162/tacl_a_00325
关键词
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资金
- ONR [N00014-17-1-2131, N00014-15-1-2209]
- NIH [1U01MH115727-01]
- NSF [CCF-1740833]
- DARPA [SD2 FA8750-18-C-0130]
- Amazon
- NVIDIA
- Simons Foundation
- EU's Horizon 2020 R&I programme under the Marie Sklodowska-Curie grant [706760]
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.
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