Recognizing Chinese Judicial Named Entity using BiLSTM-CRF

TL;DR

This paper introduces a deep learning approach using BiLSTM-CRF with Adam optimization for Chinese judicial named entity recognition, achieving high accuracy on real judicial documents.

Contribution

The paper presents a novel BiLSTM-CRF model optimized with Adam for improved Chinese judicial NER performance, addressing language and domain challenges.

Findings

Achieved 0.876 accuracy on judicial NER tasks.

Demonstrated superiority over existing methods.

Validated on real judicial documents from China Judgments Online.

Abstract

Named entity recognition (NER) plays an essential role in natural language processing systems. Judicial NER is a fundamental component of judicial information retrieval, entity relation extraction, and knowledge map building. However, Chinese judicial NER remains to be more challenging due to the characteristics of Chinese and high accuracy requirements in the judicial filed. Thus, in this paper, we propose a deep learning-based method named BiLSTM-CRF which consists of bi-directional long short-term memory (BiLSTM) and conditional random fields (CRF). For further accuracy promotion, we propose to use Adaptive moment estimation (Adam) for optimization of the model. To validate our method, we perform experiments on judgment documents including commutation, parole and temporary service outside prison, which is acquired from China Judgments Online. Experimental results achieve the accuracy of 0.876, recall of 0.856 and F1 score of 0.855, which suggests the superiority of the proposed BiLSTM-CRF with Adam optimizer.