For example, the representation of joke semantics has been fairly basic, typically computing word embedding similarities between all word pairs in a document<cite> (Yang et al., 2015)</cite> , and bear little resemblance to the way humans actually interpret humour. 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.