A Study on Stochastic Variational Inference for Topic Modeling with Word Embeddings

Kana Ozaki, Ichiro Kobayashie


Probabilistic topic models based on Latent Dirichlet Allocation (LDA) is widely used to extract latent topics from document collections. In recent years, a number of extended topic models have been proposed, especially Gaussian LDA (G-LDA) has attracted a lot of attention. G-LDA integrates topic modeling with word embeddings by replacing discrete topic distributions over words with multivariate Gaussian distributions on the word embedding space. This can reflect semantic information into topics. In this paper, we use G-LDA for our base topic model and apply Stochastic Variational Inference (SVI), an efficient inference algorithm, to estimate topics. Through experiments, we could extract the topics with high coherence in practical time.


Topic model, latent dirichlet allocation, word embeddings, stochastic variational inference

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