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2024 | OriginalPaper | Buchkapitel

A BERT-Based Model for Legal Document Proofreading

verfasst von : Jinlong Liu, Xudong Luo

Erschienen in: Intelligent Information Processing XII

Verlag: Springer Nature Switzerland

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Abstract

Legal documents require high precision and accuracy in language use, leaving no room for grammatical and spelling errors. To address the issue, this paper proposes a novel application of the BERT pre-trained language model for legal document proofreading. The BERT-based model is trained to detect and correct legal texts’ grammatical and spelling errors. On a dataset of annotated legal documents, we experimentally show that our BERT-based model significantly outperforms state-of-the-art proofreading models in precision, recall, and F1 score, showing its potential as a valuable tool in legal document preparation and revision processes. The application of such advanced deep learning techniques could revolutionise the field of legal document proofreading, enhancing accuracy and efficiency.

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Metadaten
Titel
A BERT-Based Model for Legal Document Proofreading
verfasst von
Jinlong Liu
Xudong Luo
Copyright-Jahr
2024
Buch
Intelligent Information Processing XII

Print ISBN: 978-3-031-57807-6

Electronic ISBN: 978-3-031-57808-3

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-57808-3_14

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