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
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 401–406
- Language:
- URL:
- https://aclanthology.org/S17-2067
- DOI:
- 10.18653/v1/S17-2067
- Cite (ACL):
- Andrew Cattle and Xiaojuan Ma. 2017. SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 401–406, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- SRHR at SemEval-2017 Task 6: Word Associations for Humour Recognition (Cattle & Ma, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/S17-2067.pdf