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  1. Encoded summarization: summarizing documents into continuous vector space for legal case retrieval.Vu Tran, Minh Le Nguyen, Satoshi Tojo & Ken Satoh - 2020 - Artificial Intelligence and Law 28 (4):441-467.
    We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we explore the benefits from combining lexical features and latent features generated with neural networks. Our experiments show that lexical features and latent features generated with neural networks complement each other to improve the retrieval system performance. Furthermore, our experimental results suggest the (...)
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  • Recognizing cited facts and principles in legal judgements.Olga Shulayeva, Advaith Siddharthan & Adam Wyner - 2017 - Artificial Intelligence and Law 25 (1):107-126.
    In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present (...)
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  • A history of AI and Law in 50 papers: 25 years of the international conference on AI and Law. [REVIEW]Trevor Bench-Capon, Michał Araszkiewicz, Kevin Ashley, Katie Atkinson, Floris Bex, Filipe Borges, Daniele Bourcier, Paul Bourgine, Jack G. Conrad, Enrico Francesconi, Thomas F. Gordon, Guido Governatori, Jochen L. Leidner, David D. Lewis, Ronald P. Loui, L. Thorne McCarty, Henry Prakken, Frank Schilder, Erich Schweighofer, Paul Thompson, Alex Tyrrell, Bart Verheij, Douglas N. Walton & Adam Z. Wyner - 2012 - Artificial Intelligence and Law 20 (3):215-319.
    We provide a retrospective of 25 years of the International Conference on AI and Law, which was first held in 1987. Fifty papers have been selected from the thirteen conferences and each of them is described in a short subsection individually written by one of the 24 authors. These subsections attempt to place the paper discussed in the context of the development of AI and Law, while often offering some personal reactions and reflections. As a whole, the subsections build into (...)
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  • Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings.Kevin D. Ashley & Will Bridewell - 2010 - Artificial Intelligence and Law 18 (4):311-320.
    This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify (...)
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  • A legal case OWL ontology with an instantiation of Popov v. Hayashi.Adam Wyner & Rinke Hoekstra - 2012 - Artificial Intelligence and Law 20 (1):83-107.
    The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only (...)
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  • Automatically classifying case texts and predicting outcomes.Kevin D. Ashley & Stefanie Brüninghaus - 2009 - Artificial Intelligence and Law 17 (2):125-165.
    Work on a computer program called SMILE + IBP (SMart Index Learner Plus Issue-Based Prediction) bridges case-based reasoning and extracting information from texts. The program addresses a technologically challenging task that is also very relevant from a legal viewpoint: to extract information from textual descriptions of the facts of decided cases and apply that information to predict the outcomes of new cases. The program attempts to automatically classify textual descriptions of the facts of legal problems in terms of Factors, a (...)
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  • Towards a machine understanding of Malawi legal text.Amelia V. Taylor & Eva Mfutso-Bengo - 2023 - Artificial Intelligence and Law 31 (1):1-11.
    Legal professionals in Malawi rely on a limited number of textbooks, outdated law reports and inadequate library services. Most documents available are in image form, are un-structured, i.e. contain no useful legal meta-data, summaries, keynotes, and do not support a system of citation that is essential to legal research. While advances in document processing and machine learning have benefited many fields, legal research is still only marginally affected. In this interdisciplinary research, the authors build semi-automatic tools for creating a corpus (...)
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