A neural topic model with word vectors and entity vectors for short texts

Traditional topic models are widely used for semantic discovery from long texts. However, they usually fail to mine high-quality topics from short texts (e.g. tweets) due to the sparsity of features and the lack of word co-occurrence patterns. In this paper, we propose a Variational Auto-Encoder Topic Model (VAETM for short) by combining word vector representation and entity vector representation to address the above limitations. Specifically, we first learn embedding representations of each word and each entity by employing a large-scale external corpora and a large and manually edited knowledge graph, respectively. Then we integrated the embedding representations into the variational auto-encoder framework and propose an unsupervised model named VAETM to infer the latent representation of topic distributions. To further boost VAETM, we propose an improved supervised VAETM (SVAETM for short) by considering label information in training set to supervise the inference of latent representation of topic distributions and the generation of topics. Last, we propose KL-divergence-based inference algorithms to infer approximate posterior distribution for our two models. Extensive experiments on three common short text datasets demonstrate our proposed VAETM and SVAETM outperform various kinds of state-of-the-art models in terms of perplexity, NPMI, and accuracy.