Automatically extracting word relationships as templates for pun generation

Abstract

Computational models can be built to capture the syntactic structures and semantic patterns of human punning riddles. This model is then used as rules by a computer to generate its own puns. This paper presents T-PEG, a system that utilizes phonetic and semantic linguistic resources to automatically extract word relationships in puns and store the knowledge in template form. Given a set of training examples, it is able to extract 69.2% usable templates, resulting in computer-generated puns that received an average score of 2.13 as compared to 2.70 for human-generated puns from user feedback.