SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition

Abstract

This paper explores the role of semantic relatedness features, such as word associations, in humour recognition. Specifically, we examine the task of inferring pairwise humour judgments in Twitter hashtag wars. We examine a variety of word association features derived from the University of Southern Florida Free Association Norms (USF) (Nelson et al., 2004) and the Edinburgh Associative Thesaurus (EAT) (Kiss et al., 1973) and find that word association-based features outperform Word2Vec similarity, a popular semantic relatedness measure. Our system achieves an accuracy of 56.42% using a combination of unigram perplexity, bigram perplexity, EATtweet-avgdifference, USFmaxforward, EATword-avgdifference, USFword-avgdifference, EATminforward, USFtweet-maxdifference, and EATminbackward