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.- Anthology ID:
- 2023.semeval-1.247
- 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:
- 1783–1790
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.247
- DOI:
- 10.18653/v1/2023.semeval-1.247
- Cite (ACL):
- Luis Adrián Cabrera-Diego and Akshita Gheewala. 2023. Jus Mundi at SemEval-2023 Task 6: Using a Frustratingly Easy Domain Adaption for a Legal Named Entity Recognition System. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1783–1790, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Jus Mundi at SemEval-2023 Task 6: Using a Frustratingly Easy Domain Adaption for a Legal Named Entity Recognition System (Cabrera-Diego & Gheewala, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.semeval-1.247.pdf