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

Andrew Cattle, Xiaojuan Ma


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 University of Southern Florida Free Association Norms (USF) and the Edinburgh Associative Thesaurus (EAT) 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, EAT difference (tweet-avg), USF forward (max), EAT difference (word-avg), USF difference (word-avg), EAT forward (min), USF difference (tweet-max), and EAT backward (min).
Anthology ID:
S17-2067
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
401–406
Language:
URL:
https://www.aclweb.org/anthology/S17-2067
DOI:
10.18653/v1/S17-2067
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2067.pdf