@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Low-Resource Techniques for Analysing the Rhetorical Structure of Swedish Historical Petitions
%A Lindqvist, Ellinor
%A Pettersson, Eva
%A Nivre, Joakim
%Y Ilinykh, Nikolai
%Y Morger, Felix
%Y Dannélls, Dana
%Y Dobnik, Simon
%Y Megyesi, Beáta
%Y Nivre, Joakim
%S Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Tórshavn, the Faroe Islands
%F lindqvist-etal-2023-low
%X 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.
%U https://aclanthology.org/2023.resourceful-1.16
%P 132-139
Markdown (Informal)
[Low-Resource Techniques for Analysing the Rhetorical Structure of Swedish Historical Petitions](https://aclanthology.org/2023.resourceful-1.16) (Lindqvist et al., RESOURCEFUL 2023)
ACL