  Within this study, we propose a new approach for natural language processing
using Bayesian networks to predict and analyze the context and how this
approach can be applied to the Community Question Answering domain. We discuss
how Bayesian networks can detect semantic relationships and dependencies
between entities, and this is connected to different score-based approaches of
structure-learning. We compared the Bayesian networks with different score
metrics, such as the BIC, BDeu, K2 and Chow-Liu trees. Our proposed approach
out-performs the baseline model at 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 in the structure of directed acyclic
graphs. Finally, we evaluate potential improvements for the Bayesian network
approach.
