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

Duraimurugan Rajamanickam
10/11/2024
cs.CLcs.IRcs.LG

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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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Cite this paper

@article{rajamanickam2024improving,
  title  = {Improving Legal Entity Recognition Using a Hybrid Transformer Model and Semantic Filtering Approach},
  author = {Duraimurugan Rajamanickam},
  year   = {2024},
  eprint = {2410.08521},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.08521v1}
}

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