Abstract
Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that involves identifying and categorizing named entities. The result annotation makes unstructured natural texts applicable for other NLP tasks, including information retrieval, question answering, and machine translation. NER is also essential in legal as an initial stage in extracting relevant entities. However, legal texts contain domain-specific named entities, such as applicants, defendants, courts, statutes, and articles. The latter makes standard named entity recognizers incompatible with legal documents. This paper proposes an approach combining multiple models’ results via a voting mechanism for unique entity identification in legal texts. This endeavor focuses on extracting legal named entities, and the specific assignment (task B) is to create a legal NER system for unique entity annotation in legal documents. The results of our experiments and system implementation are published in https://github.com/SuperEDG/Legal_Project.- Anthology ID:
- 2023.semeval-1.178
- 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:
- 1282–1286
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.178
- DOI:
- 10.18653/v1/2023.semeval-1.178
- Cite (ACL):
- Junzhe Zhao, Yingxi Wang, Nicolay Rusnachenko, and Huizhi Liang. 2023. Legal_try at SemEval-2023 Task 6: Voting Heterogeneous Models for Entities identification in Legal Documents. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1282–1286, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Legal_try at SemEval-2023 Task 6: Voting Heterogeneous Models for Entities identification in Legal Documents (Zhao et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-04/2023.semeval-1.178.pdf