Rhetorical Role Identification for Portuguese Legal Documents
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Intelligent Systems: 10th Brazilian Conference, BRACIS 2021, Virtual Event, November 29 – December 3, 2021, Proceedings, Part II. ed. / André Britto; Karina Valdivia Delgado. Cham: Springer Schweiz, 2021. p. 557-571 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13074 LNAI).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Rhetorical Role Identification for Portuguese Legal Documents
AU - Aragy, Roberto
AU - Fernandes, Eraldo Rezende
AU - Caceres, Edson Norberto
N1 - Conference code: 10
PY - 2021
Y1 - 2021
N2 - In this paper, we present a new corpus for Rhetorical Role Identification in Portuguese legal documents. The corpus comprises petitions from 70 civil lawsuits filed in TJMS court and was manually labeled with rhetorical roles specifically tailored for petitions. Since petition documents are created without a standard structure, we had to deal with several issues to clean the extracted textual content. We assessed classic and deep learning machine learning methods on the proposed corpus. The best performing method obtained an F-score of 80.50. At the best of our knowledge, this is the first work to deal with rhetorical role identification for petitions, given that previous works focused only on judicial decisions. Additionally, it is also the first work to tackle this task for the Portuguese language. The proposed corpus, as well as the proposed rhetorical roles, can foster new research in the judicial area and also lead to new solutions to improve the flow of Brazilian court houses.
AB - In this paper, we present a new corpus for Rhetorical Role Identification in Portuguese legal documents. The corpus comprises petitions from 70 civil lawsuits filed in TJMS court and was manually labeled with rhetorical roles specifically tailored for petitions. Since petition documents are created without a standard structure, we had to deal with several issues to clean the extracted textual content. We assessed classic and deep learning machine learning methods on the proposed corpus. The best performing method obtained an F-score of 80.50. At the best of our knowledge, this is the first work to deal with rhetorical role identification for petitions, given that previous works focused only on judicial decisions. Additionally, it is also the first work to tackle this task for the Portuguese language. The proposed corpus, as well as the proposed rhetorical roles, can foster new research in the judicial area and also lead to new solutions to improve the flow of Brazilian court houses.
KW - Corpus
KW - Legal sentence classification
KW - Natural language processing
KW - Rhetorical role identification
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85121825188&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-91699-2_38
DO - 10.1007/978-3-030-91699-2_38
M3 - Article in conference proceedings
AN - SCOPUS:85121825188
SN - 978-3-030-91698-5
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 557
EP - 571
BT - Intelligent Systems
A2 - Britto, André
A2 - Valdivia Delgado, Karina
PB - Springer Schweiz
CY - Cham
T2 - Brazilian Conference on Intelligent Systems - BRACIS 2021
Y2 - 29 November 2021 through 3 December 2021
ER -