skip to main content
10.1145/3397271.3401466acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
research-article

Legal Intelligence: Algorithmic, Data, and Social Challenges

Published:25 July 2020Publication History

ABSTRACT

In the digital era, information retrieval, text/knowledge mining, and NLP techniques are playing increasingly vital roles in legal domain. While the open datasets and innovative deep learning methodologies provide critical potentials, in the legal-domain, efforts need to be made to transfer the theoretical/algorithmic models into the real applications to assist users, lawyers, judges and the legal professions to solve the real problems. The objective of this workshop is to aggregate studies/applications of text mining/retrieval and NLP automation in the context of classical/novel legal tasks, which address algorithmic, data and social challenges of legal intelligence. Keynote and invited presentations from industry and academic will be able to fill the gap between ambition and execution in the legal domain.

References

  1. Stefanie Brüninghaus and Kevin D Ashley. 2001. Improving the representation of legal case texts with information extraction methods. (2001), 42--51.Google ScholarGoogle Scholar
  2. Ilias Chalkidis, Ion Androutsopoulos, and Nikolaos Aletras. 2019. Neural Legal Judgment Prediction in English. (July 2019), 4317--4323.Google ScholarGoogle Scholar
  3. Emmanuel Chieze, Atefeh Farzindar, and Guy Lapalme. 2010. An automatic system for summarization and information extraction of legal information. In Semantic processing of legal texts. Springer, 216--234.Google ScholarGoogle Scholar
  4. Xinyu Duan, Yating Zhang, Lin Yuan, Xin Zhou, Xiaozhong Liu, Tianyi Wang, Ruocheng Wang, Qiong Zhang, Changlong Sun, and Fei Wu. 2019. Legal Summarization for Multi-role Debate Dialogue via Controversy Focus Mining and Multi-task Learning. (2019), 1361--1370.Google ScholarGoogle Scholar
  5. Xin Jiang, Hai Ye, Zhunchen Luo, WenHan Chao, and Wenjia Ma. 2018. Interpretable rationale augmented charge prediction system. (2018), 146--151.Google ScholarGoogle Scholar
  6. Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, and Dongyan Zhao. 2017. Learning to Predict Charges for Criminal Cases with Legal Basis. (2017), 2727--2736.Google ScholarGoogle Scholar
  7. S Santhana Megala, A Kavitha, and A Marimuthu. 2014. Feature Extraction Based Legal Document Summarization. International Journal of Advance Research in Computer Science and Management Studies, Vol. 2, 12 (2014), 346--352.Google ScholarGoogle Scholar
  8. Michael Mills. 2016. Artificial Intelligence in Law: The State of Play 2016. Thomson Reuters Legal executive Institute (2016).Google ScholarGoogle Scholar
  9. Marie-Francine Moens, Caroline Uyttendaele, and Jos Dumortier. 1999. Information extraction from legal texts: the potential of discourse analysis. International Journal of Human-Computer Studies, Vol. 51, 6 (1999), 1155--1171.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Douglas W Oard, Jason R Baron, Bruce Hedin, David D Lewis, and Stephen Tomlinson. 2010. Evaluation of information retrieval for E-discovery. Artificial Intelligence and Law, Vol. 18, 4 (2010), 347--386.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Douglas W Oard, William Webber, et al. 2013. Information retrieval for e-discovery. Foundations and Trends® in Information Retrieval, Vol. 7, 2--3 (2013), 99--237.Google ScholarGoogle ScholarCross RefCross Ref
  12. OECD. 2013. What makes civil justice effective? OECD Economics Department Policy Notes 18 (2013).Google ScholarGoogle Scholar
  13. Seth Polsley, Pooja Jhunjhunwala, and Ruihong Huang. 2016. CaseSummarizer: A System for Automated Summarization of Legal Texts. (2016), 258--262.Google ScholarGoogle Scholar
  14. Richard Susskind. 2000. Transforming the law: Essays on technology, justice and the legal marketplace .Oxford University Press, Inc.Google ScholarGoogle Scholar
  15. Richard E Susskind and Richard E Susskind. 1996. The future of law: facing the challenges of information technology. Number s 279. Clarendon Press Oxford.Google ScholarGoogle Scholar
  16. Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, et al. 2018. Cail2018: A large-scale legal dataset for judgment prediction. arXiv preprint arXiv:1807.02478 (2018).Google ScholarGoogle Scholar
  17. Zhou Xin, Zhang Yating, Liu Xiaozhong, Sun Changlong, and Si Luo. 2019. Legal Intelligence for E-commerce: Multi-task Learning by Leveraging Multiview Dispute Representation. (2019).Google ScholarGoogle Scholar
  18. Hai Ye, Xin Jiang, Zhunchen Luo, and Wenhan Chao. 2018. Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions. (2018), 1854--1864.Google ScholarGoogle Scholar
  19. Haoxi Zhong, Guo Zhipeng, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, and Maosong Sun. 2018. Legal Judgment Prediction via Topological Learning. (2018), 3540--3549.Google ScholarGoogle Scholar

Index Terms

  1. Legal Intelligence: Algorithmic, Data, and Social Challenges

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
        July 2020
        2548 pages
        ISBN:9781450380164
        DOI:10.1145/3397271

        Copyright © 2020 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]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 July 2020

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate792of3,983submissions,20%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader