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

• URL: http://arxiv.org/abs/2410.08521v1
• Date: Fri, 11 Oct 2024 04:51:28 GMT
• Title: Improving Legal Entity Recognition Using a Hybrid Transformer Model and Semantic Filtering Approach
• Authors: Duraimurugan Rajamanickam,
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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