1. Improving Legal Entity Recognition Using a Hybrid Transformer Model and Semantic Filtering Approach
- Author
-
Rajamanickam, Duraimurugan
- Subjects
Computer Science - Computation and Language ,Computer Science - Information Retrieval ,Computer Science - Machine Learning - 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., Comment: 7 pages, 1 table
- Published
- 2024