An Attention Hierarchical Topic Modeling

Probabilistic topic models have been used to detect topic-based content presentations when facing a collection of documents. However, topic models capture the semantic information according to reasonable simplifying hypotheses, which ignore the worthwhile word-order information. This paper proposes an attention hierarchical topic modeling, which adopts attention mechanism to unify topic embedding and word embedding together into a framework to enhance the clustering effect of hierarchical Dirichlet process. Otherwise, the multi-information integration Chinese restaurant franchise is adopted to construct this model, which further combines timestamp, user, and topic label to optimize topic modeling. Extensive experiments on real-life applications show that our model outperforms several strong baselines on document modeling and classification.