用Transformer模型提升法律文本中违规识别与关联的准确率。
uOttawa at LegalLens-2024: Transformer-based Classification Experiments
- 结合RoBERTa与CNN处理法律文本推理任务。
- 在法律实体识别和自然语言推理上分别达86.3%和88.25%准确率。
- 代码开源，适合法律AI研究者参考。
本文介绍了参与LegalLens-2024共享任务的方法，该任务聚焦于从非结构化文本中检测法律违规行为，并将这些违规与可能受影响的个人关联。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）。针对子任务A，采用spaCy库；针对子任务B，使用融合RoBERTa与CNN的组合模型。实验结果显示，L-NER子任务准确率为86.3%，L-NLI子任务准确率为88.25%。整体表明，Transformer模型在法律领域复杂任务中具有显著有效性。实现代码已公开于https://github.com/NimaMeghdadi/uOttawa-at-LegalLens-2024-Transformer-based-Classification。
原文摘要 · Abstract (English)
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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