Recognizing cited facts and principles in legal judgements

65Citations
Citations of this article
112Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

References Powered by Scopus

1981Citations
371Readers
Get full text
Get full text

Automatic classification of citation function

408Citations
306Readers

Cited by Powered by Scopus

This article is free to access.

Get full text

LeCaRD: A Legal Case Retrieval Dataset for Chinese Law System

80Citations
38Readers
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Shulayeva, O., Siddharthan, A., & Wyner, A. (2017). Recognizing cited facts and principles in legal judgements. Artificial Intelligence and Law, 25(1), 107–126. https://doi.org/10.1007/s10506-017-9197-6

Readers over time

‘16‘17‘18‘19‘20‘21‘22‘23‘24‘2507142128

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 43

63%

Researcher 11

16%

Professor / Associate Prof. 7

10%

Lecturer / Post doc 7

10%

Readers' Discipline

Tooltip

Computer Science 28

45%

Social Sciences 23

37%

Engineering 6

10%

Economics, Econometrics and Finance 5

8%

Article Metrics

Tooltip
Mentions
Blog Mentions: 1
Social Media
Shares, Likes & Comments: 27

Save time finding and organizing research with Mendeley

Sign up for free
0