@inproceedings{ginn-khamov-2023-ginn,
title = "Ginn-Khamov at {S}em{E}val-2023 Task 6, Subtask {B}: Legal Named Entities Extraction for Heterogenous Documents",
author = "Ginn, Michael and
Khamov, Roman",
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-1.57",
doi = "10.18653/v1/2023.semeval-1.57",
pages = "420--425",
abstract = "This paper describes our submission to SemEval-2023 Task 6, Subtask B, a shared task on performing Named Entity Recognition in legal documents for specific legal entity types. Documents are divided into the preamble and judgement texts, and certain entity types should only be tagged in one of the two text sections. To address this challenge, our team proposes a token classification model that is augmented with information about the document type, which achieves greater performance than the non-augmented system.",
}
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<abstract>This paper describes our submission to SemEval-2023 Task 6, Subtask B, a shared task on performing Named Entity Recognition in legal documents for specific legal entity types. Documents are divided into the preamble and judgement texts, and certain entity types should only be tagged in one of the two text sections. To address this challenge, our team proposes a token classification model that is augmented with information about the document type, which achieves greater performance than the non-augmented system.</abstract>
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%0 Conference Proceedings
%T Ginn-Khamov at SemEval-2023 Task 6, Subtask B: Legal Named Entities Extraction for Heterogenous Documents
%A Ginn, Michael
%A Khamov, Roman
%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 ginn-khamov-2023-ginn
%X This paper describes our submission to SemEval-2023 Task 6, Subtask B, a shared task on performing Named Entity Recognition in legal documents for specific legal entity types. Documents are divided into the preamble and judgement texts, and certain entity types should only be tagged in one of the two text sections. To address this challenge, our team proposes a token classification model that is augmented with information about the document type, which achieves greater performance than the non-augmented system.
%R 10.18653/v1/2023.semeval-1.57
%U https://aclanthology.org/2023.semeval-1.57
%U https://doi.org/10.18653/v1/2023.semeval-1.57
%P 420-425
Markdown (Informal)
[Ginn-Khamov at SemEval-2023 Task 6, Subtask B: Legal Named Entities Extraction for Heterogenous Documents](https://aclanthology.org/2023.semeval-1.57) (Ginn & Khamov, SemEval 2023)
ACL