Bayesian Networks for Named Entity Prediction in Programming Community Question Answering

Alexey Gorbatovski, Sergey V. Kovalchuk

Abstract: Within this study, we propose a new approach for natural language processing using Bayesian networks to predict and analyze the context and show how this approach can be applied to the Community Question Answering domain, such as Stack Overflow questions. We compared the Bayesian networks with different score metrics, such as the BIC, BDeu, K2, and Chow-Liu trees. Our proposed approach outperforms the baseline model on the precision metric. We also discuss the influence of penalty terms on the structure of Bayesian networks and how they can be used to analyze the relationships between entities. In addition, we examine the visualization of directed acyclic graphs to analyze semantic relationships. The article further identifies issues with detecting certain semantic classes that are separated by the structure of directed acyclic graphs.