@inproceedings{janicki-etal-2023-detection,
title = "Detection and attribution of quotes in {F}innish news media: {BERT} vs. rule-based approach",
author = {Janicki, Maciej and
Kanner, Antti and
M{\"a}kel{\"a}, Eetu},
editor = {Alum{\"a}e, Tanel and
Fishel, Mark},
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.6",
pages = "52--59",
abstract = "We approach the problem of recognition and attribution of quotes in Finnish news media. Solving this task would create possibilities for large-scale analysis of media wrt. the presence and styles of presentation of different voices and opinions. We describe the annotation of a corpus of media texts, numbering around 1500 articles, with quote attribution and coreference information. Further, we compare two methods for automatic quote recognition: a rule-based one operating on dependency trees and a machine learning one built on top of the BERT language model. We conclude that BERT provides more promising results even with little training data, achieving 95{\%} F-score on direct quote recognition and 84{\%} for indirect quotes. Finally, we discuss open problems and further associated tasks, especially the necessity of resolving speaker mentions to entity references.",
}
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%0 Conference Proceedings
%T Detection and attribution of quotes in Finnish news media: BERT vs. rule-based approach
%A Janicki, Maciej
%A Kanner, Antti
%A Mäkelä, Eetu
%Y Alumäe, Tanel
%Y Fishel, Mark
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F janicki-etal-2023-detection
%X We approach the problem of recognition and attribution of quotes in Finnish news media. Solving this task would create possibilities for large-scale analysis of media wrt. the presence and styles of presentation of different voices and opinions. We describe the annotation of a corpus of media texts, numbering around 1500 articles, with quote attribution and coreference information. Further, we compare two methods for automatic quote recognition: a rule-based one operating on dependency trees and a machine learning one built on top of the BERT language model. We conclude that BERT provides more promising results even with little training data, achieving 95% F-score on direct quote recognition and 84% for indirect quotes. Finally, we discuss open problems and further associated tasks, especially the necessity of resolving speaker mentions to entity references.
%U https://aclanthology.org/2023.nodalida-1.6
%P 52-59
Markdown (Informal)
[Detection and attribution of quotes in Finnish news media: BERT vs. rule-based approach](https://aclanthology.org/2023.nodalida-1.6) (Janicki et al., NoDaLiDa 2023)
ACL