Flexible Distribution Approaches to Enhance Regression and Deep Topic Modelling Techniques

Abstract

ABSTRACTThis paper presents an extension of the Dirichlet multinomial regression (DMR) and deep Dirichlet multinomial regression (dDMR) topic modelling approaches by incorporating the generalised Dirichlet (GD) and Beta‐Liouville (BL) distributions using collapsed Gibbs sampling for parameter inference. The DMR and dDMR approaches have been shown to be effective in discovering latent topics in text corpora. However, these approaches have limitations when it comes to handling complex data structures and overfitting issues. To address these limitations, we introduce the GD and BL distributions, which have more flexibility in modelling complex data structures and handling sparse data. Additionally, we use collapsed Gibbs sampling to estimate the model parameters, which provides a computationally efficient method for inference. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed approach in improving topic modelling performance, particularly in handling complex data structures and reducing overfitting. The proposed models also exhibit good interpretability of the learned topics, making them suitable for various applications in natural language processing and machine learning.