Using the Interest Theory of Rights and Hohfeldian Taxonomy to Address a Gap in Machine Learning Methods for Legal Document Analysis

Abstract

Rights and duties are essential features of legal documents. Machine learning algorithms have been increasingly applied to extract information from such texts. Currently, their main focus is on named entity recognition, sentiment analysis, and the classification of court cases to predict court outcome. In this paper it is argued that until the essential features of such texts are captured, their analysis can remain bottle-necked by the very technology being used to assess them. As such, the use of legal theory to identify the most pertinent dimensions of such texts is proposed. Specifically, the interest theory of rights, and the first-order Hohfeldian taxonomy of legal relations. These principal legal dimensions allow for a stratified representation of knowledge, making them ideal for the abstractions needed for machine learning. This study considers how such dimensions may be identified. To do so it implements a novel heuristic based in philosophy coupled with language models. Hohfeldian relations of ‘rights-duties’ vs. ‘privileges-no-rights’ are determined to be identifiable. Classification of each type of relation to accuracies of 92.5% is found using Sentence Bidirectional Encoder Representations from Transformers. Testing is carried out on religious discrimination policy texts in the United Kingdom.

Keywords: Hohfeld, Interest Theory, Golden Rule, Artificial Intelligence, Natural Language Processing, Machine Learning, Discrimination, Annotation