@inproceedings{stranisci-etal-2023-wikibio,
title = "{W}iki{B}io: a Semantic Resource for the Intersectional Analysis of Biographical Events",
author = "Stranisci, Marco Antonio and
Damiano, Rossana and
Mensa, Enrico and
Patti, Viviana and
Radicioni, Daniele and
Caselli, Tommaso",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.691",
doi = "10.18653/v1/2023.acl-long.691",
pages = "12370--12384",
abstract = "Biographical event detection is a relevant task that allows for the exploration and comparison of the ways in which people{'}s lives are told and represented. This may support several real-life applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was aligned with 5 existing corpora in order to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.",
}
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<abstract>Biographical event detection is a relevant task that allows for the exploration and comparison of the ways in which people’s lives are told and represented. This may support several real-life applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was aligned with 5 existing corpora in order to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.</abstract>
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%0 Conference Proceedings
%T WikiBio: a Semantic Resource for the Intersectional Analysis of Biographical Events
%A Stranisci, Marco Antonio
%A Damiano, Rossana
%A Mensa, Enrico
%A Patti, Viviana
%A Radicioni, Daniele
%A Caselli, Tommaso
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F stranisci-etal-2023-wikibio
%X Biographical event detection is a relevant task that allows for the exploration and comparison of the ways in which people’s lives are told and represented. This may support several real-life applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was aligned with 5 existing corpora in order to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.
%R 10.18653/v1/2023.acl-long.691
%U https://aclanthology.org/2023.acl-long.691
%U https://doi.org/10.18653/v1/2023.acl-long.691
%P 12370-12384
Markdown (Informal)
[WikiBio: a Semantic Resource for the Intersectional Analysis of Biographical Events](https://aclanthology.org/2023.acl-long.691) (Stranisci et al., ACL 2023)
ACL