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.- Anthology ID:
- 2023.semeval-1.57
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 420–425
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.57
- DOI:
- 10.18653/v1/2023.semeval-1.57
- Cite (ACL):
- Michael Ginn and Roman Khamov. 2023. Ginn-Khamov at SemEval-2023 Task 6, Subtask B: Legal Named Entities Extraction for Heterogenous Documents. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 420–425, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Ginn-Khamov at SemEval-2023 Task 6, Subtask B: Legal Named Entities Extraction for Heterogenous Documents (Ginn & Khamov, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.semeval-1.57.pdf