Understanding Satirical Articles Using Common-Sense

Dan Goldwasser     Xiao Zhang    
Transactions of the Association for Computational Linguistics (TACL), 2016


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.

Bib Entry

    author = "Dan Goldwasser and Xiao Zhang",
    title = "Understanding Satirical Articles Using Common-Sense",
    booktitle = "Transactions of the Association for Computational Linguistics (TACL)",
    year = "2016"