Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery

Abstract

Hierarchical Dirichlet Process (HDP) has attracted much attention in the research community of natural language processing. Given a corpus, HDP is able to determine the number of topics automatically, possessing an important feature dubbed nonparametric that overcomes the challenging issue of manually specifying a suitable topic number in parametric topic models, such as Latent Dirichlet Allocation (LDA). Nevertheless, HDP requires a much higher computational cost than LDA for parameter estimation. By taking the advantage of multi-threading, a parallel Gibbs sampling algorithm is proposed to estimate parameters for HDP based on the equivalence between HDP and Gamma-Gamma Poisson Process (G2PP) in terms of the generative process. Unfortunately, the above parallel Gibbs sampling algorithm requires to apply the finite approximation on the number of topics manually (i.e., predefine the topic number), thus can not retain the nonparametric feature of HDP. Another drawback of the above models is the lack of capturing the semantic dependencies between words, because the topic assignment of words is independent with each other. Although some works have been done in phrase-based topic modelling, these existing methods are still limited by either enforcing the entire phrase to share a common topic or requiring much complex and time-consuming phrase mining methods. In this paper, we aim to develop a copula guided parallel Gibbs sampling algorithm for HDP which can adjust the number of topics dynamically and capture the latent semantic dependencies between words that compose a coherent segment. Extensive experiments on real-world datasets indicate that our method achieves low perplexities and high topic coherence scores with a small time cost. In addition, we validate the effectiveness of our method on the modelling of word semantic dependencies by comparing the extracted topical phrases with those learned by state-of-the-art phrase-based baselines.