Named Entity Recognition Method of Chinese Legal Documents Based on Parallel Instance Query Network

Legal Named Entity Recognition (NER) is crucial in intelligent judiciary systems, focusing on identifying case-specific entities in legal texts. It helps convert unstructured legal documents into structured data, improving e-discovery efficiency. However, challenges arise from insufficient understanding of legal terminology, leading to errors in identifying long and nested entity boundaries. To address this, a Legal NER method based on a parallel instance query network is proposed. This method uses learnable instance queries to extract entities in parallel, with a BERT+BiLSTM+attention structure to encode context and query information. Entity prediction is performed using a pointer network to identify span boundaries and entity types. A linear label assignment mechanism aligns legal entities with queries for more accurate labeling. Experimental results show that the model outperforms existing methods, and further validation through ablation experiments and case studies supports its effectiveness, offering valuable insights for advancing legal NER research.