@inproceedings{garcia-corral-etal-2024-politicause,
    title = "{P}oliti{C}ause: An Annotation Scheme and Corpus for Causality in Political Texts",
    author = "Garcia Corral, Paulina  and
      Bechara, Hanna  and
      Zhang, Ran  and
      Jankin, Slava",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1124",
    pages = "12836--12845",
    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.",
}

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.
Garcia Corral, Paulina Bechara, Hanna Zhang, Ran Jankin, Slava
PolitiCause: An Annotation Scheme and Corpus for Causality in Political Texts