@inproceedings{sierro-etal-2024-automatic,
title = "Automatic Detection and Labelling of Personal Data in Case Reports from the {ECHR} in {S}panish: Evaluation of Two Different Annotation Approaches",
author = "Sierro, Maria and
Altuna, Bego{\~n}a and
Gonzalez-Dios, Itziar",
editor = {Volodina, Elena and
Alfter, David and
Dobnik, Simon and
Lindstr{\"o}m Tiedemann, Therese and
Mu{\~n}oz S{\'a}nchez, Ricardo and
Szawerna, Maria Irena and
Vu, Xuan-Son},
booktitle = "Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.caldpseudo-1.3",
pages = "18--24",
abstract = "In this paper we evaluate two annotation approaches for automatic detection and labelling of personal information in legal texts in relation to the ambiguity of the labels and the homogeneity of the annotations. For this purpose, we built a corpus of 44 case reports from the European Court of Human Rights in Spanish language and we annotated it following two different annotation approaches: automatic projection of the annotations of an existing English corpus, and manual annotation with our reinterpretation of their guidelines. Moreover, we employ Flair on a Named Entity Recognition task to compare its performance in the two annotation schemes.",
}
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<abstract>In this paper we evaluate two annotation approaches for automatic detection and labelling of personal information in legal texts in relation to the ambiguity of the labels and the homogeneity of the annotations. For this purpose, we built a corpus of 44 case reports from the European Court of Human Rights in Spanish language and we annotated it following two different annotation approaches: automatic projection of the annotations of an existing English corpus, and manual annotation with our reinterpretation of their guidelines. Moreover, we employ Flair on a Named Entity Recognition task to compare its performance in the two annotation schemes.</abstract>
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%0 Conference Proceedings
%T Automatic Detection and Labelling of Personal Data in Case Reports from the ECHR in Spanish: Evaluation of Two Different Annotation Approaches
%A Sierro, Maria
%A Altuna, Begoña
%A Gonzalez-Dios, Itziar
%Y Volodina, Elena
%Y Alfter, David
%Y Dobnik, Simon
%Y Lindström Tiedemann, Therese
%Y Muñoz Sánchez, Ricardo
%Y Szawerna, Maria Irena
%Y Vu, Xuan-Son
%S Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F sierro-etal-2024-automatic
%X In this paper we evaluate two annotation approaches for automatic detection and labelling of personal information in legal texts in relation to the ambiguity of the labels and the homogeneity of the annotations. For this purpose, we built a corpus of 44 case reports from the European Court of Human Rights in Spanish language and we annotated it following two different annotation approaches: automatic projection of the annotations of an existing English corpus, and manual annotation with our reinterpretation of their guidelines. Moreover, we employ Flair on a Named Entity Recognition task to compare its performance in the two annotation schemes.
%U https://aclanthology.org/2024.caldpseudo-1.3
%P 18-24
Markdown (Informal)
[Automatic Detection and Labelling of Personal Data in Case Reports from the ECHR in Spanish: Evaluation of Two Different Annotation Approaches](https://aclanthology.org/2024.caldpseudo-1.3) (Sierro et al., CALD-pseudo-WS 2024)
ACL