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

翻译：本文介绍了为LegalLens-2024共享任务所采用的方法。该任务聚焦于从非结构化文本数据中检测法律违规行为，并将这些违规行为与可能受影响的个体相关联。该共享任务包含两个子任务：A) 法律命名实体识别（L-NER）和 B) 法律自然语言推理（L-NLI）。对于子任务A，我们使用了spaCy库；对于子任务B，我们采用了一个结合了RoBERTa与CNN的混合模型。我们在L-NER子任务中取得了86.3%的准确率，在L-NLI子任务中取得了88.25%的准确率。总体而言，本文展示了Transformer模型在处理法律领域复杂任务方面的有效性。我们实现的源代码已在https://github.com/NimaMeghdadi/uOttawa-at-LegalLens-2024-Transformer-based-Classification 公开。