Copula Guided Neural Topic Modelling for Short Texts

Abstract

Extracting the topical information from documents is important for public opinion analysis, text classification, and information retrieval tasks. Compared with identifying a wide variety of topics from long documents, it is challenging to generate a concentrated topic distribution for each short message. Although this problem can be tackled by adjusting the hyper-parameters in traditional topic models such as Latent Dirichlet Allocation, it remains an open problem in neural topic modelling. In this paper, we focus on adapting the popular Auto-Encoding Variational Bayes based neural topic models to short texts, by exploring the Archimedean copulas to guide the estimated topic distributions derived from linear projected samples of re-parameterized posterior distributions. Experimental results show the superiority of our method when compared with existing neural topic models in terms of perplexity, topic coherence, and classification accuracy.