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.