Legal information extraction and classification using BERT, Bi-LSTM, and CRF models

作者：Jingwei Li · 发表于：Journal of Computational Methods in Sciences and Engineering · 年份：2025 · DOI：10.1177/14727978251323131 · 被引用次数：3 · 研究领域：Advanced Text Analysis Techniques、Data Quality and Management、Topic Modeling

This article combined BERT (Bidirectional Encoder Representation from Transformers), Bi-LSTM (Bidirectional Long Short-Term Memory), and CRF (Conditional Random Field) models to transform unstructured legal text into structured data through information extraction, improving the effectiveness of legal information extraction. The BERT model can be used for deep semantic embedding of legal texts, generating context-sensitive representations for each word. The Bi-LSTM network can capture long-distance dependencies in the text, extract sequence features, and apply CRF layers to globally optimize sequence labels to ensure accurate annotation of entity boundaries and relationships. In the dataset for extracting legal entity relationships related to prostitution constructed in this article, the accuracy, precision, recall rate, and F1 score of entity classification reached 93.6%, 92.7%, 92.1%, and 92.4%, respectively. All 153 samples in the Engage_in_ prostitution relationship were correctly classified. In order to analyze the stability of legal information extraction and classification, the model proposed by this article was tested on five datasets: CAIL2019, CJRC (Chinese Judicial Reading Comprehension), LexGLUE (Legal General Language Understanding Evaluation), COLIEE (Competition on Legal Information Extraction/Appointment), and ECHR (European Court of Human Rights). The accuracy of the article’s model fluctuated only 1.2% on different datasets, while the precision remained stabl...