Understanding Satirical Articles Using Common-Sense

Dan Goldwasser, Xiao Zhang


Abstract
Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories, thus allowing the model to adapt to previously unseen situations.
Anthology ID:
Q16-1038
Volume:
Transactions of the Association for Computational Linguistics, Volume 4
Month:
Year:
2016
Address:
Cambridge, MA
Editors:
Lillian Lee, Mark Johnson, Kristina Toutanova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
537–549
Language:
URL:
https://aclanthology.org/Q16-1038
DOI:
10.1162/tacl_a_00116
Bibkey:
Cite (ACL):
Dan Goldwasser and Xiao Zhang. 2016. Understanding Satirical Articles Using Common-Sense. Transactions of the Association for Computational Linguistics, 4:537–549.
Cite (Informal):
Understanding Satirical Articles Using Common-Sense (Goldwasser & Zhang, TACL 2016)
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 https://preview.aclanthology.org/ingest-bitext-workshop/Q16-1038.mp4