@inproceedings{bocchi-palmero-aprosio-2024-title,
title = "Title Is (Not) All You Need for {E}uro{V}oc Multi-Label Classification of {E}uropean Laws",
author = "Bocchi, Lorenzo and
Palmero Aprosio, Alessio",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.10/",
pages = "74--80",
ISBN = "979-12-210-7060-6",
abstract = "Machine Learning and Artificial Intelligence approaches within Public Administration (PA) have grown significantly in recent years. Specifically, new guidelines from various governments recommend employing the EuroVoc thesaurus for the classification of documents issued by the PA.In this paper, we explore some methods to perform document classification in the legal domain, in order to mitigate the length limitation for input texts in BERT models.We first collect data from the European Union, already tagged with the aforementioned taxonomy.Then we reorder the sentences included in the text, with the aim of bringing the most informative part of the document in the first part of the text.Results show that the title and the context are both important, although the order of the text may not.Finally, we release on GitHub both the dataset and the source code used for the experiments."
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%0 Conference Proceedings
%T Title Is (Not) All You Need for EuroVoc Multi-Label Classification of European Laws
%A Bocchi, Lorenzo
%A Palmero Aprosio, Alessio
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F bocchi-palmero-aprosio-2024-title
%X Machine Learning and Artificial Intelligence approaches within Public Administration (PA) have grown significantly in recent years. Specifically, new guidelines from various governments recommend employing the EuroVoc thesaurus for the classification of documents issued by the PA.In this paper, we explore some methods to perform document classification in the legal domain, in order to mitigate the length limitation for input texts in BERT models.We first collect data from the European Union, already tagged with the aforementioned taxonomy.Then we reorder the sentences included in the text, with the aim of bringing the most informative part of the document in the first part of the text.Results show that the title and the context are both important, although the order of the text may not.Finally, we release on GitHub both the dataset and the source code used for the experiments.
%U https://aclanthology.org/2024.clicit-1.10/
%P 74-80
Markdown (Informal)
[Title Is (Not) All You Need for EuroVoc Multi-Label Classification of European Laws](https://aclanthology.org/2024.clicit-1.10/) (Bocchi & Palmero Aprosio, CLiC-it 2024)
ACL