Abstract
This paper presents the methods used for LegalLens-2024, which focused on detecting legal violations within unstructured textual data and associating these violations with potentially affected individuals. The shared task included two subtasks: A) Legal Named Entity Recognition (L-NER) and B) Legal Natural Language Inference (L-NLI). For subtask A, we utilized the spaCy library, while for subtask B, we employed a combined model incorporating RoBERTa and CNN. Our results were 86.3% in the L-NER subtask and 88.25% in the L-NLI subtask. Overall, our paper demonstrates the effectiveness of transformer models in addressing complex tasks in the legal domain.- Anthology ID:
- 2024.nllp-1.4
- Volume:
- Proceedings of the Natural Legal Language Processing Workshop 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, FL, USA
- Editors:
- Nikolaos Aletras, Ilias Chalkidis, Leslie Barrett, Cătălina Goanță, Daniel Preoțiuc-Pietro, Gerasimos Spanakis
- Venue:
- NLLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 42–47
- Language:
- URL:
- https://aclanthology.org/2024.nllp-1.4
- DOI:
- 10.18653/v1/2024.nllp-1.4
- Cite (ACL):
- Nima Meghdadi and Diana Inkpen. 2024. uOttawa at LegalLens-2024: Transformer-based Classification Experiments. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 42–47, Miami, FL, USA. Association for Computational Linguistics.
- Cite (Informal):
- uOttawa at LegalLens-2024: Transformer-based Classification Experiments (Meghdadi & Inkpen, NLLP 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.nllp-1.4.pdf