Computer Science

Using the interest theory of rights and Hohfeldian taxonomy to address a gap in machine learning methods for legal document analysis

A. Izzidien

This research conducted by Ahmed Izzidien explores the limitations of machine learning algorithms in legal document analysis, arguing that they fail to capture critical features like rights and duties. By incorporating the interest theory of rights and Hohfeldian taxonomy into stratified knowledge representation, it implements a groundbreaking heuristic that achieves a remarkable 92.5% accuracy in identifying essential relations within UK religious discrimination policy texts.

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Abstract

This paper argues that machine learning (ML) algorithms for legal document analysis are bottlenecked by not capturing essential features like rights and duties. It proposes using the interest theory of rights and Hohfeldian taxonomy to identify these features for stratified knowledge representation in ML. A novel heuristic based on philosophy and language models is implemented to identify Hohfeldian relations ('rights-duties' vs. 'privileges-no-rights'), achieving 92.5% accuracy using Sentence-BERT on UK religious discrimination policy texts.

Publisher

Humanities and Social Sciences Communications

Published On

May 19, 2023

Authors

Ahmed Izzidien

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