research-article

Automated Bundle Pagination Using Machine Learning

Authors:

Alessandro Torrisi,

Katie Atkinson,

Danushka Bollegala,

Frans CoenenAuthors Info & Claims

ICAIL '19: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

Pages 244 - 248

https://doi.org/10.1145/3322640.3326726

Published: 17 June 2019 Publication History

Abstract

Coherent division of legal document bundles, whether this is done in the context of court bundles, briefs or some other application, is a time consuming and challenging task. We propose an approach whereby this process can be automated. Two variations are considered. The first addresses the scenario where the topic labelling is pre-defined and adopts a supervised learning approach. The second addresses the scenario where the topic labelling, for whatever reason, is not specified in advance and adopts an unsupervised learning approach. This paper reports on an investigation of both mechanisms using accident claims bundles. The evaluation results indicate that the proposed approaches can be successfully applied to divide legal document bundles.

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Digital Library

Cited By

Trofimov EMetsker OPaskoshev D(2021)Administrative prejudice in cases of petty theft (the Article 7.27 of the Code of the Russian Federation on Administrative Offenses and the Article 158.1 of the Criminal Code of the Russian Federation): how the big data of judicial acts reflect humanization and quality of justiceЮридические исследования10.25136/2409-7136.2021.9.36521(81-124)Online publication date: Sep-2021
https://doi.org/10.25136/2409-7136.2021.9.36521
Metsker OTrofimov EKopanitsa G(2021)Application of Machine Learning Metrics for Dynamic E-justice Processes2021 28th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT50888.2021.9347598(293-300)Online publication date: 27-Jan-2021
https://doi.org/10.23919/FRUCT50888.2021.9347598

Index Terms

Automated Bundle Pagination Using Machine Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Conferences

ICAIL '19: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

June 2019

312 pages

ISBN:9781450367547

DOI:10.1145/3322640

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Univ. of Montreal: University of Montreal
AAAI
IAAIL: Intl Asso for Artifical Intel & Law

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2019

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ICAIL '19

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SIGAI

ICAIL '19: Seventeenth International Conference on Artificial Intelligence and Law

June 17 - 21, 2019

QC, Montreal, Canada

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Overall Acceptance Rate 69 of 169 submissions, 41%

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Cited By

Trofimov EMetsker OPaskoshev D(2021)Administrative prejudice in cases of petty theft (the Article 7.27 of the Code of the Russian Federation on Administrative Offenses and the Article 158.1 of the Criminal Code of the Russian Federation): how the big data of judicial acts reflect humanization and quality of justiceЮридические исследования10.25136/2409-7136.2021.9.36521(81-124)Online publication date: Sep-2021
https://doi.org/10.25136/2409-7136.2021.9.36521
Metsker OTrofimov EKopanitsa G(2021)Application of Machine Learning Metrics for Dynamic E-justice Processes2021 28th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT50888.2021.9347598(293-300)Online publication date: 27-Jan-2021
https://doi.org/10.23919/FRUCT50888.2021.9347598

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