  In order for our computer systems to be more human-like, with a higher
emotional quotient, they need to be able to process and understand intrinsic
human language phenomena like humour. In this paper, we consider a subtype of
humour - puns, which are a common type of wordplay-based jokes. In particular,
we consider code-mixed puns which have become increasingly mainstream on social
media, in informal conversations and advertisements and aim to build a system
which can automatically identify the pun location and recover the target of
such puns. We first study and classify code-mixed puns into two categories
namely intra-sentential and intra-word, and then propose a four-step algorithm
to recover the pun targets for puns belonging to the intra-sentential category.
Our algorithm uses language models, and phonetic similarity-based features to
get the desired results. We test our approach on a small set of code-mixed
punning advertisements, and observe that our system is successfully able to
recover the targets for 67% of the puns.
