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Recognizing cited facts and principles in legal judgements

Shulayeva, Olga; Siddharthan, Advaith and Wyner, Adam (2017). Recognizing cited facts and principles in legal judgements. Artificial Intelligence and Law, 25(1) pp. 107–126.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/s10506-017-9197-6
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Abstract

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 studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, κ=0.65 and κ=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall κ of 0.72 with the human-annotated gold standard.

Item Type: Journal Item
Copyright Holders: 2017 The Authors
ISSN: 1572-8382
Keywords: Legal judgements; Citations; Natural language processing
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 51051
Depositing User: Advaith Siddharthan
Date Deposited: 21 Sep 2017 08:12
Last Modified: 25 Sep 2017 09:24
URI: http://oro.open.ac.uk/id/eprint/51051
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