LegalSeg：通过修辞角色分类解锁印度法律判决的结构
LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification
AI总结：
本文针对印度法律判决的语义分割任务提出LegalSeg大型标注数据集，评估多种先进模型后发现融合上下文等信息的模型性能更优，为法律NLP研究奠定基础。
摘要：
本文针对法律文档的语义分割任务，聚焦印度法律判决，开展修辞角色分类研究。我们引入LegalSeg——该任务下规模最大的标注数据集，包含7000余篇文档和140万余句，标注有7种修辞角色。为进行性能基准测试，我们评估了多种最先进模型，包括分层BiLSTM-CRF、TransformerOverInLegalBERT（ToInLegalBERT）、图神经网络（GNNs）、角色感知Transformer，以及探索性的RhetoricLLaMA（一款指令微调的大型语言模型）。结果显示，融合更广泛上下文、结构关系和句子序列信息的模型，性能优于仅依赖句子级特征的模型。此外，我们通过周围上下文及相邻句子的预测或实际标签开展实验，评估其对分类准确率的影响。尽管取得上述进展，区分密切相关角色和解决类别不平衡仍面临挑战。本研究凸显了先进技术在提升法律文档理解方面的潜力，为法律自然语言处理的未来研究奠定了坚实基础。
英文摘要：
In this paper, we address the task of semantic segmentation of legal documents through rhetorical role classification, with a focus on Indian legal judgments. We introduce LegalSeg, the largest annotated dataset for this task, comprising over 7,000 documents and 1.4 million sentences, labeled with 7 rhetorical roles. To benchmark performance, we evaluate multiple state-of-the-art models, including Hierarchical BiLSTM-CRF, TransformerOverInLegalBERT (ToInLegalBERT), Graph Neural Networks (GNNs), and Role-Aware Transformers, alongside an exploratory RhetoricLLaMA, an instruction-tuned large language model. Our results demonstrate that models incorporating broader context, structural relationships, and sequential sentence information outperform those relying solely on sentence-level features. Additionally, we conducted experiments using surrounding context and predicted or actual labels of neighboring sentences to assess their impact on classification accuracy. Despite these advancements, challenges persist in distinguishing between closely related roles and addressing class imbalance. Our work underscores the potential of advanced techniques for improving legal document understanding and sets a strong foundation for future research in legal NLP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
