Current Volume 10
Abstract
Legal documents are complex and unstructured, making manual extraction of important entities inefficient and error-prone. This project presents an automated Named Entity Recognition (NER) system for legal documents using a fine-tuned Legal-BERT model. The system identifies key entities such as persons, organizations, dates, locations, and legal provisions. A Streamlit-based web application enables users to upload documents and view extracted entities interactively. The proposed solution reduces manual effort and improves the efficiency of legal document analysis.
Keywords
Legal NER, Legal-BERT, NLP, Legal Documents, Streamlit
Citations
IRE Journals:
Surendran S, Pragadesh Kumar G S, Preetham M V, Sushanthi "Named Entity Recognition On Legal Documents Using Legal Bert" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 621-623 https://doi.org/10.64388/IREV9I10-1716069
IEEE:
Surendran S, Pragadesh Kumar G S, Preetham M V, Sushanthi
"Named Entity Recognition On Legal Documents Using Legal Bert" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026, doi: https://doi.org/10.64388/IREV9I10-1716069
APA:
Surendran S, Pragadesh Kumar G S, Preetham M V, Sushanthi
(2026). Named Entity Recognition On Legal Documents Using Legal Bert. Iconic Research And Engineering Journals, 9(10). doi: https://doi.org/10.64388/IREV9I10-1716069
MLA:
Surendran S, Pragadesh Kumar G S, Preetham M V, Sushanthi
"Named Entity Recognition On Legal Documents Using Legal Bert" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026. Crossref, https://doi.org/10.64388/IREV9I10-1716069
BibTeX
@article{1716069,
author = {Surendran S, Pragadesh Kumar G S, Preetham M V, Sushanthi},
title = {Named Entity Recognition On Legal Documents Using Legal Bert},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {621-623},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716069.pdf},
abstract = {Legal documents are complex and unstructured, making manual extraction of important entities inefficient and error-prone. This project presents an automated Named Entity Recognition (NER) system for legal documents using a fine-tuned Legal-BERT model. The system identifies key entities such as persons, organizations, dates, locations, and legal provisions. A Streamlit-based web application enables users to upload documents and view extracted entities interactively. The proposed solution reduces manual effort and improves the efficiency of legal document analysis.},
keywords = {Legal NER, Legal-BERT, NLP, Legal Documents, Streamlit},
month = {April}
}
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