The escalating number of pending cases is a growing concern world-wide. Recent advancements in digitization have opened up possibilities for leveraging artificial intelligence (AI) tools in the processing of legal documents. Adopting a structured representation for legal documents, as opposed to a mere bag-of-words flat text representation, can significantly enhance processing capabilities. With the aim of achieving this objective, we put forward a set of diverse attributes for criminal case proceedings. We use a state-of-the-art sequence labeling framework to automatically extract attributes from the legal documents. Moreover, we demonstrate the efficacy of the extracted attributes in a downstream task, namely legal judgment prediction.
@article{arxiv.2310.12131,
title = {Automated Attribute Extraction from Legal Proceedings},
author = {Subinay Adhikary and Sagnik Das and Sagnik Saha and Procheta Sen and Dwaipayan Roy and Kripabandhu Ghosh},
journal= {arXiv preprint arXiv:2310.12131},
year = {2023}
}
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Presented in Mining and Learning in the Legal Domain (MLLD) workshop 2023