@inproceedings{smadu-etal-2022-legal,
title = "Legal Named Entity Recognition with Multi-Task Domain Adaptation",
author = "Sm{\u{a}}du, R{\u{a}}zvan-Alexandru and
Dinic{\u{a}}, Ion-Robert and
Avram, Andrei-Marius and
Cercel, Dumitru-Clementin and
Pop, Florin and
Cercel, Mihaela-Claudia",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-Pietro, Daniel",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nllp-1.29",
doi = "10.18653/v1/2022.nllp-1.29",
pages = "305--321",
abstract = "Named Entity Recognition (NER) is a well-explored area from Information Retrieval and Natural Language Processing with an extensive research community. Despite that, few languages, such as English and German, are well-resourced, whereas many other languages, such as Romanian, have scarce resources, especially in domain-specific applications. In this work, we address the NER problem in the legal domain from both Romanian and German languages and evaluate the performance of our proposed method based on domain adaptation. We employ multi-task learning to jointly train a neural network on two legal and general domains and perform adaptation among them. The results show that domain adaptation increase performances by a small amount, under 1{\%}, while considerable improvements are in the recall metric.",
}
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<abstract>Named Entity Recognition (NER) is a well-explored area from Information Retrieval and Natural Language Processing with an extensive research community. Despite that, few languages, such as English and German, are well-resourced, whereas many other languages, such as Romanian, have scarce resources, especially in domain-specific applications. In this work, we address the NER problem in the legal domain from both Romanian and German languages and evaluate the performance of our proposed method based on domain adaptation. We employ multi-task learning to jointly train a neural network on two legal and general domains and perform adaptation among them. The results show that domain adaptation increase performances by a small amount, under 1%, while considerable improvements are in the recall metric.</abstract>
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%0 Conference Proceedings
%T Legal Named Entity Recognition with Multi-Task Domain Adaptation
%A Smădu, Răzvan-Alexandru
%A Dinică, Ion-Robert
%A Avram, Andrei-Marius
%A Cercel, Dumitru-Clementin
%A Pop, Florin
%A Cercel, Mihaela-Claudia
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F smadu-etal-2022-legal
%X Named Entity Recognition (NER) is a well-explored area from Information Retrieval and Natural Language Processing with an extensive research community. Despite that, few languages, such as English and German, are well-resourced, whereas many other languages, such as Romanian, have scarce resources, especially in domain-specific applications. In this work, we address the NER problem in the legal domain from both Romanian and German languages and evaluate the performance of our proposed method based on domain adaptation. We employ multi-task learning to jointly train a neural network on two legal and general domains and perform adaptation among them. The results show that domain adaptation increase performances by a small amount, under 1%, while considerable improvements are in the recall metric.
%R 10.18653/v1/2022.nllp-1.29
%U https://aclanthology.org/2022.nllp-1.29
%U https://doi.org/10.18653/v1/2022.nllp-1.29
%P 305-321
Markdown (Informal)
[Legal Named Entity Recognition with Multi-Task Domain Adaptation](https://aclanthology.org/2022.nllp-1.29) (Smădu et al., NLLP 2022)
ACL
- Răzvan-Alexandru Smădu, Ion-Robert Dinică, Andrei-Marius Avram, Dumitru-Clementin Cercel, Florin Pop, and Mihaela-Claudia Cercel. 2022. Legal Named Entity Recognition with Multi-Task Domain Adaptation. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 305–321, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.