@inproceedings{radev-etal-2016-humor,
title = "Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest",
author = "Radev, Dragomir and
Stent, Amanda and
Tetreault, Joel and
Pappu, Aasish and
Iliakopoulou, Aikaterini and
Chanfreau, Agustin and
de Juan, Paloma and
Vallmitjana, Jordi and
Jaimes, Alejandro and
Jha, Rahul and
Mankoff, Robert",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1076",
pages = "475--479",
abstract = "The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.",
}
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%0 Conference Proceedings
%T Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest
%A Radev, Dragomir
%A Stent, Amanda
%A Tetreault, Joel
%A Pappu, Aasish
%A Iliakopoulou, Aikaterini
%A Chanfreau, Agustin
%A de Juan, Paloma
%A Vallmitjana, Jordi
%A Jaimes, Alejandro
%A Jha, Rahul
%A Mankoff, Robert
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 may
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F radev-etal-2016-humor
%X The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.
%U https://aclanthology.org/L16-1076
%P 475-479
Markdown (Informal)
[Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest](https://aclanthology.org/L16-1076) (Radev et al., LREC 2016)
ACL
- Dragomir Radev, Amanda Stent, Joel Tetreault, Aasish Pappu, Aikaterini Iliakopoulou, Agustin Chanfreau, Paloma de Juan, Jordi Vallmitjana, Alejandro Jaimes, Rahul Jha, and Robert Mankoff. 2016. Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 475–479, Portorož, Slovenia. European Language Resources Association (ELRA).