PolitiCause: An Annotation Scheme and Corpus for Causality in Political Texts

Paulina Garcia Corral, Hanna Bechara, Ran Zhang, Slava Jankin

Abstract
In this paper, we present PolitiCAUSE, a new corpus of political texts annotated for causality. We provide a detailed and robust annotation scheme for annotating two types of information: (1) whether a sentence contains a causal relation or not, and (2) the spans of text that correspond to the cause and effect components of the causal relation. We also provide statistics and analysis of the corpus, and outline the difficulties and limitations of the task. Finally, we test out two transformer-based classification models on our dataset as a form of evaluation. The models achieve a moderate performance on the dataset, with a MCC score of 0.62. Our results show that PolitiCAUSE is a valuable resource for studying causality in texts, especially in the domain of political discourse, and that there is still room for improvement in developing more accurate and robust methods for this problem.
Anthology ID:
2024.lrec-main.1124
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
12836–12845
Language:
URL:
https://aclanthology.org/2024.lrec-main.1124
DOI:
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Cite (ACL):
Paulina Garcia Corral, Hanna Bechara, Ran Zhang, and Slava Jankin. 2024. PolitiCause: An Annotation Scheme and Corpus for Causality in Political Texts. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12836–12845, Torino, Italia. ELRA and ICCL.
Cite (Informal):
PolitiCause: An Annotation Scheme and Corpus for Causality in Political Texts (Garcia Corral et al., LREC-COLING 2024)
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