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.
Cite
CITATION STYLE
Cabrera-Diego, L. A., & Gheewala, A. (2023). Jus Mundi at SemEval-2023 Task 6: Using a Frustratingly Easy Domain Adaption for a Legal Named Entity Recognition System. In 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop (pp. 1783–1790). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.semeval-1.247
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