This paper aims to marry the interpretability of statistical machine learning approaches with the more nuanced models of joke structure and joke semantics of neural approaches.
we explore the effectiveness modelling joke semantics using semantic relatedness features based on word associations, rather than the more common semantic similarity features based on word embeddings.