Improving Legal Entity Recognition Using a Hybrid Transformer Model and Semantic Filtering Approach

Abstract

Legal Entity Recognition (LER) is critical in automating legal workflows such as contract analysis, compliance monitoring, and litigation support. Existing approaches, including rule-based systems and classical machine learning models, struggle with the complexity of legal documents and domain specificity, particularly in handling ambiguities and nested entity structures. This paper proposes a novel hybrid model that enhances the accuracy and precision of Legal-BERT, a transformer model fine-tuned for legal text processing, by introducing a semantic similarity-based filtering mechanism. We evaluate the model on a dataset of 15,000 annotated legal documents, achieving an F1 score of 93.4%, demonstrating significant improvements in precision and recall over previous methods.