uOttawa at LegalLens-2024: Transformer-based Classification Experiments

• URL: http://arxiv.org/abs/2410.21139v2
• Date: Thu, 31 Oct 2024 03:29:15 GMT
• Title: uOttawa at LegalLens-2024: Transformer-based Classification Experiments
• Authors: Nima Meghdadi, Diana Inkpen,
• Abstract summary: This paper presents the methods used for LegalLens-2024 shared task. It focused on detecting legal violations within unstructured textual data and associating these violations with potentially affected individuals. Our results were 86.3% in the L-NER subtask and 88.25% in the L-NLI subtask.
• Score: 7.710171464368831
• License:
• Abstract: This paper presents the methods used for LegalLens-2024 shared task, 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. The source code for our implementation is publicly available at https://github.com/NimaMeghdadi/uOttawa-at-LegalLens-2024-Transformer-based-Classification

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