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In this paper we present a model for unsupervised topic discovery in textscorpora. The proposed model uses documents, words, and topics lookup tableembedding as neural network model parameters to build probabilities of wordsgiven topics, and probabilities of topics given documents. These probabilitiesare used to recover by marginalization probabilities of words given documents.For very large corpora where the number of documents can be in the order ofbillions, using a neural auto-encoder based document embedding is more scalablethen using a lookup table embedding as classically done. We thus extended thelookup based document embedding model to continuous auto-encoder based model.Our models are trained using probabilistic latent semantic analysis (PLSA)assumptions. We evaluated our models on six datasets with a rich variety ofcontents. Conducted experiments demonstrate that the proposed neural topicmodels are very effective in capturing relevant topics. Furthermore,considering perplexity metric, conducted evaluation benchmarks show that ourtopic models outperform latent Dirichlet allocation (LDA) model which isclassically used to address topic discovery tasks.

Discovering topics with neural topic models built from PLSA assumptions