uOttawa at LegalLens-2024: Transformer-based Classification Experiments
This work addresses legal document analysis for practitioners, but it is incremental as it applies existing methods to a new shared task.
The paper tackled the detection of legal violations and affected individuals in text using transformer models, achieving 86.3% in legal named entity recognition and 88.25% in legal natural language inference.
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