[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128469-en":3,"doc-seo-128469-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128469,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Using the Interest Theory of Rights and Hohfeldian Taxonomy to Address a Gap in Machine Learning Methods for Legal Document Analysis","Rights and duties are core features of legal documents, yet most machine-learning approaches focus on narrower tasks such as named entity recognition, sentiment analysis, and predicting court outcomes. This paper argues that analysis becomes bottlenecked when essential document features are not captured by the modeling objective. It proposes using legal theory to identify key dimensions, specifically the interest theory of rights and the first-order Hohfeldian taxonomy of legal relations, to support stratified knowledge representations for machine learning. A heuristic paired with language models identifies Hohfeldian relations (rights–duties vs privileges–no-rights) and achieves 92.5% accuracy on UK religious discrimination policy texts.","Using the interest theory of rights and Hohfeldian taxonomy to address a gap in machine learning methods for legal document analysis  \nAhmed Izzidien [ai297@cam.ac.uk The Faculty of Law](ai297@cam.ac.uk The Faculty of Law).  \nThe University of Cambridge.  \nAbstract  \nRights 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.  \nKeywords: legal, rights, no-rights, duties, privileges, machine learning.  \nIntroduction  \nArtificial intelligence (AI), and specifically Natural Language Processing (NLP) through Machine Learning (ML) is increasingly being utilised in public interest technologies, such as in government departments, courts, and NGOs offering legal services (de Sousa et al., 2019) . Using a systematic protocol for literature reviews and meta-analysis (PRISMA) a study (de Sousa et al., 2019) demonstrated that managers of public organisations have considerably increased the adoption of AIbased systems, which in turn improve efficiency (Mehr et al., 2017) . It has been found that a month’s work at US Department for Labour can be completed in a day, at a higher accuracy, for example. Machines take into account factors at orders of magnitude greater than people without tiring. Indeed, this human frailty, when not checked, has been found to be a factor that negatively impacts decisions, such as with court rulings (Danziger et al., 2011) .  \nIt has further been found that implementing ML in such contexts has made procedures more efficient. The Supreme Court of Brazil found AI contributed positively to its procedural speed, for example (de Sousa et al., 2022) . Big data NLP also allows for a systematic analysis of documents at scale, allowing for new findings to materialise which are typically not possible with the human eye (Nay, 2018) . Indeed, almost all law is expressed in natural language; therefore NLP may be said to be a key component of understanding and predicting law at scale (Nay, 2018) .  \nLegal documents may be characterised as essentially texts which describe power interactions in society with consideration to the outcomes of such interactions (Boswell & Smith, 2017; Oliver &  \nCairney, 2019). These power interactions exist to further the interests of one or more parties involved (Hewitt, 2009; Michael Hallsworth et al., 2011) .  \nBased on the nature of these interests, rights and duties and their concomitant legal relations become imposed (M. Kramer, 2001, 2017) . Given that policy documents, which find themselves into legislation through a well-documented process (Parliament, 2021) are based on a consideration of the interests of parties affected, and the legal aspects of these interests (Policy Exchange, 2016), it is of note that to date no specific software explicitly c","cbCaibSPeXTKwmoW","https://ap.wps.com/l/cbCaibSPeXTKwmoW","pdf",3334386,1,37,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Motivation and gap in current ML for legal texts\n## Proposed legal-theory dimensions for ML representations\n## Method overview and evaluation setup","[{\"question\":\"Why does the paper claim current machine-learning methods are insufficient for legal document analysis?\",\"answer\":\"Because they often focus on tasks like named entity recognition, sentiment analysis, and case classification without capturing essential features such as rights, duties, and their legal relations, which can limit explanatory and practical effectiveness.\"},{\"question\":\"Which legal theories are proposed to identify principal dimensions of legal documents?\",\"answer\":\"The paper proposes the interest theory of rights and the first-order Hohfeldian taxonomy of legal relations to provide relevant dimensions for machine-learning abstractions.\"},{\"question\":\"How are Hohfeldian relations identified and evaluated in the study?\",\"answer\":\"The study implements a novel heuristic based on philosophy coupled with language models to determine identifiable Hohfeldian relations, then classifies relation types and reports 92.5% accuracy using a Sentence-BERT model.\"}]","Using the Interest Theory of Rights and Hohfeldian Taxonomy to Address a Gap in Machine Learning Methods for Legal Document Analysis | 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does the paper claim current machine-learning methods are insufficient for legal document analysis?","Question",{"text":76,"@type":77},"Because they often focus on tasks like named entity recognition, sentiment analysis, and case classification without capturing essential features such as rights, duties, and their legal relations, which can limit explanatory and practical effectiveness.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which legal theories are proposed to identify principal dimensions of legal documents?",{"text":81,"@type":77},"The paper proposes the interest theory of rights and the first-order Hohfeldian taxonomy of legal relations to provide relevant dimensions for machine-learning abstractions.",{"name":83,"@type":74,"acceptedAnswer":84},"How are Hohfeldian relations identified and evaluated in the study?",{"text":85,"@type":77},"The study implements a novel heuristic based on philosophy coupled with language models to determine identifiable Hohfeldian 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