Recognizing Counterfactual Thinking in Social Media Texts
Youngseo Son, Anneke Buffone, Joe Raso, Allegra Larche, Anthony Janocko, Kevin Zembroski, H Andrew Schwartz, Lyle Ungar
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.- Anthology ID:
- P17-2103
- Original:
- P17-2103v1
- Version 2:
- P17-2103v2
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 654–658
- Language:
- URL:
- https://aclanthology.org/P17-2103
- DOI:
- 10.18653/v1/P17-2103
- Cite (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.
- Cite (Informal):
- Recognizing Counterfactual Thinking in Social Media Texts (Son et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P17-2103.pdf