@inproceedings{cabrera-diego-gheewala-2023-jus,
title = "Jus Mundi at {S}em{E}val-2023 Task 6: Using a Frustratingly Easy Domain Adaption for a Legal Named Entity Recognition System",
author = "Cabrera-Diego, Luis Adri{\'a}n and
Gheewala, Akshita",
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-1.247",
doi = "10.18653/v1/2023.semeval-1.247",
pages = "1783--1790",
abstract = "In this work, we present a Named Entity Recognition (NER) system that was trained using a Frustratingly Easy Domain Adaptation (FEDA) over multiple legal corpora. The goal was to create a NER capable of detecting 14 types of legal named entities in Indian judgments. Besides the FEDA architecture, we explored a method based on overlapping context and averaging tensors to process long input texts, which can be beneficial when processing legal documents. The proposed NER reached an F1-score of 0.9007 in the sub-task B of Semeval-2023 Task 6, Understanding Legal Texts.",
}
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%0 Conference Proceedings
%T Jus Mundi at SemEval-2023 Task 6: Using a Frustratingly Easy Domain Adaption for a Legal Named Entity Recognition System
%A Cabrera-Diego, Luis Adrián
%A Gheewala, Akshita
%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 cabrera-diego-gheewala-2023-jus
%X In this work, we present a Named Entity Recognition (NER) system that was trained using a Frustratingly Easy Domain Adaptation (FEDA) over multiple legal corpora. The goal was to create a NER capable of detecting 14 types of legal named entities in Indian judgments. Besides the FEDA architecture, we explored a method based on overlapping context and averaging tensors to process long input texts, which can be beneficial when processing legal documents. The proposed NER reached an F1-score of 0.9007 in the sub-task B of Semeval-2023 Task 6, Understanding Legal Texts.
%R 10.18653/v1/2023.semeval-1.247
%U https://aclanthology.org/2023.semeval-1.247
%U https://doi.org/10.18653/v1/2023.semeval-1.247
%P 1783-1790
Markdown (Informal)
[Jus Mundi at SemEval-2023 Task 6: Using a Frustratingly Easy Domain Adaption for a Legal Named Entity Recognition System](https://aclanthology.org/2023.semeval-1.247) (Cabrera-Diego & Gheewala, SemEval 2023)
ACL