@inproceedings{navarretta-haltrup-hansen-2024-government,
    title = "Government and Opposition in {D}anish Parliamentary Debates",
    author = "Navarretta, Costanza  and
      Haltrup Hansen, Dorte",
    editor = "Fiser, Darja  and
      Eskevich, Maria  and
      Bordon, David",
    booktitle = "Proceedings of the IV Workshop on Creating, Analysing, and Increasing Accessibility of Parliamentary Corpora (ParlaCLARIN) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.parlaclarin-1.23",
    pages = "154--162",
    abstract = "In this paper, we address government and opposition speeches made by the Danish Parliament{'}s members from 2014 to 2022. We use the linguistic annotations and metadata in ParlaMint-DK, one of the ParlaMint corpora, to investigate some characteristics of the transcribed speeches made by government and opposition and test how well classifiers can identify the speeches delivered by these groups. Our analyses confirm that there are differences in the speeches made by government and opposition e.g., in the frequency of some modality expressions. In our study, we also include parties, which do not directly support or are against the government, the {``}other{''} group. The best performing classifier for identifying speeches made by parties in government, in opposition or in {``}other{''} is a transformer with a pre-trained Danish BERT model which gave an F1-score of 0.64. The same classifier obtained an F1-score of 0.77 on the binary identification of speeches made by government or opposition parties.",
}

In this paper, we address government and opposition speeches made by the Danish Parliament{'}s members from 2014 to 2022. We use the linguistic annotations and metadata in ParlaMint-DK, one of the ParlaMint corpora, to investigate some characteristics of the transcribed speeches made by government and opposition and test how well classifiers can identify the speeches delivered by these groups. Our analyses confirm that there are differences in the speeches made by government and opposition e.g., in the frequency of some modality expressions. In our study, we also include parties, which do not directly support or are against the government, the {``}other{''} group. The best performing classifier for identifying speeches made by parties in government, in opposition or in {``}other{''} is a transformer with a pre-trained Danish BERT model which gave an F1-score of 0.64. The same classifier obtained an F1-score of 0.77 on the binary identification of speeches made by government or opposition parties.
Navarretta, Costanza Haltrup Hansen, Dorte
Government and Opposition in Danish Parliamentary Debates