@inproceedings{son-etal-2017-recognizing,
title = "Recognizing Counterfactual Thinking in Social Media Texts",
author = "Son, Youngseo and
Buffone, Anneke and
Raso, Joe and
Larche, Allegra and
Janocko, Anthony and
Zembroski, Kevin and
Schwartz, H Andrew and
Ungar, Lyle",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2103",
doi = "10.18653/v1/P17-2103",
pages = "654--658",
abstract = "Counterfactual statements, describing events that did not occur and their consequents, have been studied in areas including problem-solving, affect management, and behavior regulation. People with more counterfactual thinking tend to perceive life events as more personally meaningful. Nevertheless, counterfactuals have not been studied in computational linguistics. We create a counterfactual tweet dataset and explore approaches for detecting counterfactuals using rule-based and supervised statistical approaches. A combined rule-based and statistical approach yielded the best results (F1 = 0.77) outperforming either approach used alone.",
}
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<abstract>Counterfactual statements, describing events that did not occur and their consequents, have been studied in areas including problem-solving, affect management, and behavior regulation. People with more counterfactual thinking tend to perceive life events as more personally meaningful. Nevertheless, counterfactuals have not been studied in computational linguistics. We create a counterfactual tweet dataset and explore approaches for detecting counterfactuals using rule-based and supervised statistical approaches. A combined rule-based and statistical approach yielded the best results (F1 = 0.77) outperforming either approach used alone.</abstract>
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%0 Conference Proceedings
%T Recognizing Counterfactual Thinking in Social Media Texts
%A Son, Youngseo
%A Buffone, Anneke
%A Raso, Joe
%A Larche, Allegra
%A Janocko, Anthony
%A Zembroski, Kevin
%A Schwartz, H. Andrew
%A Ungar, Lyle
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F son-etal-2017-recognizing
%X Counterfactual statements, describing events that did not occur and their consequents, have been studied in areas including problem-solving, affect management, and behavior regulation. People with more counterfactual thinking tend to perceive life events as more personally meaningful. Nevertheless, counterfactuals have not been studied in computational linguistics. We create a counterfactual tweet dataset and explore approaches for detecting counterfactuals using rule-based and supervised statistical approaches. A combined rule-based and statistical approach yielded the best results (F1 = 0.77) outperforming either approach used alone.
%R 10.18653/v1/P17-2103
%U https://aclanthology.org/P17-2103
%U https://doi.org/10.18653/v1/P17-2103
%P 654-658
Markdown (Informal)
[Recognizing Counterfactual Thinking in Social Media Texts](https://aclanthology.org/P17-2103) (Son et al., ACL 2017)
ACL
- Youngseo Son, Anneke Buffone, Joe Raso, Allegra Larche, Anthony Janocko, Kevin Zembroski, H Andrew Schwartz, and Lyle Ungar. 2017. Recognizing Counterfactual Thinking in Social Media Texts. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 654–658, Vancouver, Canada. Association for Computational Linguistics.