A BERT-Based Deep Model for Contract Element Extraction with Multi-Scale Attention
This conference paper proposes a BERT-based deep learning model with multi-scale attention for contract element extraction in legal text analysis. The work addresses limitations of keyword matching and template-based approaches by modeling contextual information, clause dependencies, and legal expression structures to improve the accuracy and practical usability of contract element extraction.
Presented as a poster at the 2026 4th IEEE International Conference on Pattern Recognition, Machine Vision and Artificial Intelligence (PRMVAI 2026). Authors visible on the poster include Ning Mou, Shuo Han, Xiu-Fu Ye, and Fan Wu.
基于BERT并结合多尺度注意力的合同要素抽取深度模型
本研究面向合同文本分析中的合同要素抽取任务，提出一种以 BERT 为基础、结合多尺度注意力机制的深度学习模型。针对传统关键词匹配和模板比对方法难以处理复杂条款、隐藏责任和长距离依赖的问题，该模型通过捕捉上下文信息、条款间依赖关系与法律表达结构，提升合同要素抽取的准确率和实用性。
该论文在2026年第四届IEEE模式识别、机器视觉与人工智能国际会议（PRMVAI 2026）上以海报形式发表。海报列出的作者包括 Ning Mou、Shuo Han、Xiu-Fu Ye、Fan Wu。