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How Could Semantic Processing and Other NLP Tools Improve Online Legal Databases? Cover

How Could Semantic Processing and Other NLP Tools Improve Online Legal Databases?

By: Renátó Vági  
Open Access
|Dec 2023

References

  1. Ashley, K. D. (2019), Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age, Cambridge, etc.: Cambridge University Press.
  2. Bing, J. (2010), ‘Let there be LITE: A Brief History of Legal Information Retrieval,’ European Journal of Law and Technology, vol. 1, no. 1.
  3. Blei, D. M.; Ngy, A. Y. & Jordan, M. I. (2003), ‘Latent Dirichlet allocation,’ Journal of Machine Learning Research, vol. 3, pp. 993–1022.
  4. Bloomberg Law (2020), Litigators Sound Off on Their Most Time-Consuming Task, 7 February. Retrieved from https://pro.bloomberglaw.com/brief/litigators-sound-off-on-their-most-time-consuming-task/ [accessed Oct 2023]
  5. Bommarito II, M. J.; Katz, D. M. & Detterman, E. M. (2021), ‘LexNLP: Natural Language Processing and Information Extraction for Legal and Regulatory Texts,’ in Research Handbook on Big Data Law, Cheltenham: Edward Elgar Publishing, pp. 216–227. https://doi.org/10.4337/9781788972826.00017
  6. Bordino, I.; Ferretti, A.; Gullo, F. & Pascolutti, S. (2021), ‘GarNLP: A Natural Language Processing Pipeline for Garnishment Documents,’ Information Systems Frontiers, vol. 23, no. 1, pp. 101–114. https://doi.org/10.1007/s10796-020-09997-0
  7. Chieze, E.; Farzindar, A. & Lapalme, G. (2010), ‘An Automatic System for Summarization and Information Extraction of Legal Information,’ in E. Francesconi et al. (eds.) Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language, Berlin: Springer, pp. 216–234. https://doi.org/10.1007/978-3-642-12837-0_12
  8. Csányi, G. M.; Vági, R.; Nagy, D.; Üveges, I.; Vadász, J. P.; Megyeri, A. & Orosz, T. (2022), ‘Building a Production-Ready Multi-Label Classifier for Legal Documents with Digital-Twin-Distiller,’ Applied Sciences, vol. 12, no. 3, art. 1470. https://doi.org/10.3390/app12031470
  9. Custers, B. (2018), ‘Methods of Data Research for Law,’ in V. Mak, E. Tjong Tjin Tai & A. Berlee (eds.) Research Handbook in Data Science and Law, Research Handbooks in Information Law, Cheltenham: Edward Elgar. https://doi.org/10.4337/9781788111300.00023
  10. Deerwester, S.; Dumais, S. T.; Furnas, G. W.; Landauer, T. K. & Harshman, R. (1990), ‘Indexing by Latent Semantic Analysis,’ Journal of the American Society of Information Science, vol. 41, no. 6, pp. 391–407. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
  11. Dhanani, J.; Mehta, R. & Rana, D. (2021), ‘Legal Document Recommendation System: A Cluster Based Pairwise Similarity Computation,’ Journal of Intelligent & Fuzzy Systems (Preprint), pp. 1–13. https://doi.org/10.3233/JIFS-189871
  12. Doslu, M. & Bingol, H. O. (2016), ‘Context Sensitive Article Ranking with Citation Context Analysis,’ Scientometrics, vol. 108, no. 2, pp. 653–671. https://doi.org/10.1007/s11192-016-1982-6
  13. Francesconi, E.; Montemagni, S.; Peters, W. & Tiscornia, D., eds. (2010), ‘Preface,’ in Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language, Berlin: Springer. https://doi.org/10.1007/978-3-642-12837-0
  14. Francesconi, E. & Peruginelli, G. (2008), ‘Integrated Access to Legal Literature through Automated Semantic Classification,’ Artificial Intelligence and Law, vol. 17, no. 1, pp. 31–49. https://doi.org/10.1007/s10506-008-9072-6
  15. Heller, J. & Arredondo, P. (2021), ‘AI in Legal Research: How AI Is Providing Everyone Access to Information and Leveling the Playing Field for Firms of All Sizes,’ in N. Waisberg & A. Hudek (eds.) AI for Lawyers, Hoboken, NJ: John Wiley & Sons, Inc.
  16. Iftikhar, A.; Ul Qounain Jaffry, S. W. & Malik, M. K. (2019), ‘Information Mining from Criminal Judgments of Lahore High Court,’ in IEEE Access, vol. 7, pp. 59539–59547. https://doi.org/10.1109/ACCESS.2019.2915352
  17. Kalva, S. & Geldon, F. (2021), ‘Semantic NLP Technologies in Information Retrieval Systems for Legal Research,’ Advances in Machine Learning & Artificial Intelligence, vol. 2, no. 1, pp. 28–32. https://doi.org/10.33140/AMLAI.02.01.05
  18. Kanapala, A.; Jannu, S. & Pamula, R. (2019), ‘Summarization of Legal Judgments Using Gravitational Search Algorithm,’ Neural Computing and Applications, vol. 31, no. 12, pp. 8631–8639. https://doi.org/10.1007/s00521-019-04177-x
  19. Katz, D. M. (2021), ‘AI + Law. An Overview,’ in D. M. Katz, R. Dolin & M. J. Bommarito (eds.) Legal Informatics, Cambridge: Cambridge University Press, pp. 358–359. https://doi.org/10.1017/9781316529683.009
  20. Koniaris, M.; Papastefanatos, G. & Anagnostopoulos, I. (2018), ‘Solon: A Holistic Approach for Modelling, Managing and Mining Legal Sources,’ Algorithms, vol. 11, no. 12, art. 196. https://doi.org/10.3390/a11120196
  21. Margolis, E. & Murray, K. E. (2012), ‘Say Goodbye to the Books: Information Literacy as the New Legal Research Paradigm,’ Temple University Legal Studies Research Paper Series, no. 2012-34. https://doi.org/10.2139/ssrn.2125278
  22. McCarty, T. (2009), ‘Remarks on Legal Text Processing—Parsing, Semantics and Information Extraction,’ in Proceedings of the Workshop on Natural Language Engineering of Legal Argumentation, Barcelona, Spain.
  23. Nadeau, D. & Sekine, S. (2007), ‘A Survey of Named Entity Recognition and Classification,’ Lingvisticæ Investigationes: International Journal of Linguistics and Language Resources, vol. 30, no. 1, pp. 3–26. https://doi.org/10.1075/li.30.1.03nad
  24. Nanda, R.; Siragusa, G.; Di Caro, L.; Boella, G.; Grossio, L.; Gerbaudo, M. & Costamagna, F. (2019), ‘Unsupervised and Supervised Text Similarity Systems for Automated Identification of National Implementing Measures of European Directives,’ Artificial Intelligence and Law, vol. 27, no. 2, pp. 199–225. https://doi.org/10.1007/s10506-018-9236-y
  25. Olsen, H. P. & Küçüksu, A. (2017), ‘Finding Hidden Patterns in ECtHR’s Case Law: On How Citation Network Analysis Can Improve Our Knowledge of ECtHR’s Article 14 Practice,’ International Journal of Discrimination and the Law, vol. 17, no. 1, pp. 4–22. https://doi.org/10.1177/1358229117693715
  26. Orosz, T.; Vági, R.; Csányi, G. M.; Nagy, D.; Üveges, I.; Vadász, J. P. & Megyeri, A. (2021), ‘Evaluating Human Versus Machine Learning Performance in a LegalTech Problem,’ Applied Sciences, vol. 12, no. 1. https://doi.org/10.3390/app12010297
  27. Robertson, S. (2004), ‘Understanding Inverse Document Frequency: On Theoretical Arguments for IDF,’ Journal of Documentation, vol. 60, no. 5, pp. 503–520. https://doi.org/10.1108/00220410410560582
  28. Sakhaee, N. & Wilson, M. C. (2020), ‘Information Extraction Framework to Build Legislation Network,’ Artificial Intelligence and Law, vol. 29, no. 1, pp. 35–58. https://doi.org/10.1007/s10506-020-09263-3
  29. Sharafat, S.; Nasar, Z. & Jaffry, S. W. (2019), ‘Data Mining for Smart Legal Systems,’ Computers & Electrical Engineering, vol. 78, pp. 328–342. https://doi.org/10.1016/j.compeleceng.2019.07.017
  30. Sleimi, A.; Sannier, N.; Sabetzadeh, M.; Briand, L.; Ceci, M. & Dann, J. (2021), ‘An Automated Framework for The Extraction of Semantic Legal Metadata from Legal Texts,’ Empirical Software Engineering, vol. 26, no. 3, art. 43. https://doi.org/10.1007/s10664-020-09933-5
  31. Trappey, C. V.; Trappey, A. J. & Liu, B.-H. (2020), ‘Identify Trademark Legal Case Precedents—Using Machine Learning to Enable Semantic Analysis of Judgments,’ World Patent Information, vol. 62, art. 101980. https://doi.org/10.1016/j.wpi.2020.101980
  32. Walters, E. & Asjes, J. (2021), ‘Fastcase, and the Visual Understanding of Judicial Precedents,’ in D. M. Katz, R. Dolin & M. J. Bommarito (eds.) Legal Informatics, Cambridge: Cambridge University Press, pp. 358–359. https://doi.org/10.1017/9781316529683.024
  33. Webb, J. (2020), ‘Legal Technology: The Great Disruption?’ University of Melbourne Legal Studies Research Paper no. 897. https://doi.org/10.2139/ssrn.3664476
  34. Zeni, N.; Kiyavitskaya, N.; Mich, L.; Cordy, J. R. & Mylopoulos, J. (2013), ‘GaiusT: Supporting the Extraction of Rights and Obligations for Regulatory Compliance,’ Requirements Engineering, vol. 20, no. 1, pp. 1–22. https://doi.org/10.1007/s00766-013-0181-8
DOI: https://doi.org/10.2478/bjes-2023-0018 | Journal eISSN: 2674-4619 | Journal ISSN: 2674-4600
Language: English
Page range: 138 - 151
Published on: Dec 9, 2023
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2023 Renátó Vági, published by Tallinn University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.