@inproceedings{noviello-etal-2023-teamunibo,
title = "{T}eam{U}nibo at {S}em{E}val-2023 Task 6: A transformer based approach to Rhetorical Roles prediction and {NER} in Legal Texts",
author = "Noviello, Yuri and
Pallotta, Enrico and
Pinzarrone, Flavio and
Tanzi, Giuseppe",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.37",
doi = "10.18653/v1/2023.semeval-1.37",
pages = "275--284",
abstract = "This study aims to tackle some challenges posed by legal texts in the field of NLP. The LegalEval challenge proposes three tasks, based on Indial Legal documents: Rhetorical Roles Prediction, Legal Named Entity Recognition, and Court Judgement Prediction with Explanation. Our work focuses on the first two tasks. For the first task we present a context-aware approach to enhance sentence information. With the help of this approach, the classification model utilizing InLegalBert as a transformer achieved 81.12{\%} Micro-F1. For the second task we present a NER approach to extract and classify entities like names of petitioner, respondent, court or statute of a given document. The model utilizing XLNet as transformer and a dependency parser on top achieved 87.43{\%} Macro-F1.",
}
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<abstract>This study aims to tackle some challenges posed by legal texts in the field of NLP. The LegalEval challenge proposes three tasks, based on Indial Legal documents: Rhetorical Roles Prediction, Legal Named Entity Recognition, and Court Judgement Prediction with Explanation. Our work focuses on the first two tasks. For the first task we present a context-aware approach to enhance sentence information. With the help of this approach, the classification model utilizing InLegalBert as a transformer achieved 81.12% Micro-F1. For the second task we present a NER approach to extract and classify entities like names of petitioner, respondent, court or statute of a given document. The model utilizing XLNet as transformer and a dependency parser on top achieved 87.43% Macro-F1.</abstract>
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%0 Conference Proceedings
%T TeamUnibo at SemEval-2023 Task 6: A transformer based approach to Rhetorical Roles prediction and NER in Legal Texts
%A Noviello, Yuri
%A Pallotta, Enrico
%A Pinzarrone, Flavio
%A Tanzi, Giuseppe
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F noviello-etal-2023-teamunibo
%X This study aims to tackle some challenges posed by legal texts in the field of NLP. The LegalEval challenge proposes three tasks, based on Indial Legal documents: Rhetorical Roles Prediction, Legal Named Entity Recognition, and Court Judgement Prediction with Explanation. Our work focuses on the first two tasks. For the first task we present a context-aware approach to enhance sentence information. With the help of this approach, the classification model utilizing InLegalBert as a transformer achieved 81.12% Micro-F1. For the second task we present a NER approach to extract and classify entities like names of petitioner, respondent, court or statute of a given document. The model utilizing XLNet as transformer and a dependency parser on top achieved 87.43% Macro-F1.
%R 10.18653/v1/2023.semeval-1.37
%U https://aclanthology.org/2023.semeval-1.37
%U https://doi.org/10.18653/v1/2023.semeval-1.37
%P 275-284
Markdown (Informal)
[TeamUnibo at SemEval-2023 Task 6: A transformer based approach to Rhetorical Roles prediction and NER in Legal Texts](https://aclanthology.org/2023.semeval-1.37) (Noviello et al., SemEval 2023)
ACL