@inproceedings{lindqvist-etal-2023-low,
    title = "Low-Resource Techniques for Analysing the Rhetorical Structure of {S}wedish Historical Petitions",
    author = "Lindqvist, Ellinor  and
      Pettersson, Eva  and
      Nivre, Joakim",
    editor = "Ilinykh, Nikolai  and
      Morger, Felix  and
      Dann{\'e}lls, Dana  and
      Dobnik, Simon  and
      Megyesi, Be{\'a}ta  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)",
    month = may,
    year = "2023",
    address = "T{\'o}rshavn, the Faroe Islands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2023.resourceful-1.16/",
    pages = "132--139",
    abstract = "Natural language processing techniques can be valuable for improving and facilitating historical research. This is also true for the analysis of petitions, a source which has been relatively little used in historical research. However, limited data resources pose challenges for mainstream natural language processing approaches based on machine learning. In this paper, we explore methods for automatically segmenting petitions according to their rhetorical structure. We find that the use of rules, word embeddings, and especially keywords can give promising results for this task."
}