MDM-NER: A multiple dependency modeling driven named entity recognition approach for judicial documents

Abstract

The characteristic of nested entities spanning a wide range in judicial documents poses significant challenges for entity recognition tasks. This paper proposes multiple dependency modeling driven named entity recognition model (MDM-NER), which can capture the association relationships between characters and words through the encoder module integrating Multi-Head Attention (MHA) and Cross-Attention (CA), and realize multi-dimensional collaborative recognition and label sequence optimization of nested entities through the decoder module composed of a joint predictor and Conditional Random Field model (CRF). It has demonstrated better comprehensive performance compared to existing models in comparative experiments when applied to Chinese corpora, English corpora, and constructed Judicial Document Corpus (JudDC), proving its adaptability, robustness, and transferability. In addition, the effectiveness of the significant components integrated attention (MHA-CA) and CRF was verified through ablation experiments, and the influence of two hyperparameters, quantity of heads and dilation rate, on the performance of model was discussed. As the key preliminary step, the proposed MDM-NER and constructed JudDR can be applied to the construction of judicial document knowledge graph, and the local deployment & data expansion tasks of vertical LLMs for judicial authorities.