@inproceedings{maladry-etal-2022-irony,
title = "Irony Detection for {D}utch: a Venture into the Implicit",
author = "Maladry, Aaron and
Lefever, Els and
Van Hee, Cynthia and
Hoste, Veronique",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Barriere, Valentin and
Tafreshi, Shabnam and
Alqahtani, Sawsan and
Sedoc, Jo{\~a}o and
Klinger, Roman and
Balahur, Alexandra",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wassa-1.16",
doi = "10.18653/v1/2022.wassa-1.16",
pages = "172--181",
abstract = "This paper presents the results of a replication experiment for automatic irony detection in Dutch social media text, investigating both a feature-based SVM classifier, as was done by Van Hee et al. (2017) and and a transformer-based approach. In addition to building a baseline model, an important goal of this research is to explore the implementation of common-sense knowledge in the form of implicit sentiment, as we strongly believe that common-sense and connotative knowledge are essential to the identification of irony and implicit meaning in tweets. We show promising results and the presented approach can provide a solid baseline and serve as a staging ground to build on in future experiments for irony detection in Dutch.",
}
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%0 Conference Proceedings
%T Irony Detection for Dutch: a Venture into the Implicit
%A Maladry, Aaron
%A Lefever, Els
%A Van Hee, Cynthia
%A Hoste, Veronique
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Alqahtani, Sawsan
%Y Sedoc, João
%Y Klinger, Roman
%Y Balahur, Alexandra
%S Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F maladry-etal-2022-irony
%X This paper presents the results of a replication experiment for automatic irony detection in Dutch social media text, investigating both a feature-based SVM classifier, as was done by Van Hee et al. (2017) and and a transformer-based approach. In addition to building a baseline model, an important goal of this research is to explore the implementation of common-sense knowledge in the form of implicit sentiment, as we strongly believe that common-sense and connotative knowledge are essential to the identification of irony and implicit meaning in tweets. We show promising results and the presented approach can provide a solid baseline and serve as a staging ground to build on in future experiments for irony detection in Dutch.
%R 10.18653/v1/2022.wassa-1.16
%U https://aclanthology.org/2022.wassa-1.16
%U https://doi.org/10.18653/v1/2022.wassa-1.16
%P 172-181
Markdown (Informal)
[Irony Detection for Dutch: a Venture into the Implicit](https://aclanthology.org/2022.wassa-1.16) (Maladry et al., WASSA 2022)
ACL
- Aaron Maladry, Els Lefever, Cynthia Van Hee, and Veronique Hoste. 2022. Irony Detection for Dutch: a Venture into the Implicit. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 172–181, Dublin, Ireland. Association for Computational Linguistics.