Recognizing Humour using Word Associations and Humour Anchor Extraction

Andrew Cattle, Xiaojuan Ma


Abstract
This paper attempts to marry the interpretability of statistical machine learning approaches with the more robust models of joke structure and joke semantics capable of being learned by neural models. Specifically, we explore the use of semantic relatedness features based on word associations, rather than the more common Word2Vec similarity, on a binary humour identification task and identify several factors that make word associations a better fit for humour. We also explore the effects of using joke structure, in the form of humour anchors (Yang et al., 2015), for improving the performance of semantic features and show that, while an intriguing idea, humour anchors contain several pitfalls that can hurt performance.
Anthology ID:
C18-1157
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1849–1858
Language:
URL:
https://aclanthology.org/C18-1157
DOI:
Bibkey:
Cite (ACL):
Andrew Cattle and Xiaojuan Ma. 2018. Recognizing Humour using Word Associations and Humour Anchor Extraction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1849–1858, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Recognizing Humour using Word Associations and Humour Anchor Extraction (Cattle & Ma, COLING 2018)
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PDF:
https://aclanthology.org/C18-1157.pdf