Summary of Improving Legal Entity Recognition Using a Hybrid Transformer Model and Semantic Filtering Approach, by Duraimurugan Rajamanickam
Improving Legal Entity Recognition Using a Hybrid Transformer Model and Semantic Filtering Approach
by Duraimurugan Rajamanickam
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel hybrid model for Legal Entity Recognition (LER), which is crucial for automating legal workflows such as contract analysis, compliance monitoring, and litigation support. The proposed model 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. By evaluating this model on a dataset of 15,000 annotated legal documents, it achieves an F1 score of 93.4%, demonstrating significant improvements in precision and recall over previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how computers can understand legal documents. Currently, computers struggle to find specific information within these documents because they’re very complex and often contain ambiguities. The researchers developed a new model that combines two different approaches to better recognize legal entities. They tested this model on a large dataset of annotated legal documents and found it was much more accurate than previous methods. |
Keywords
» Artificial intelligence » Bert » F1 score » Precision » Recall » Transformer
