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Artificial Intelligence Applied in Legal Information: A Systematic Mapping Study
DOI:
https://doi.org/10.24215/16666038.25.e03Keywords:
Artificial Intelligence (AI), civil law, legal information retrieval, automatic recovery, text classification algorithmsAbstract
In the context of the digital era, our lives have become more dynamic, and the development of advanced technologies is transforming the way civil law relationships are handled. Today, legal professionals commonly use software that accelerates daily processes through the application of artificial intelligence technologies. Legal Information Retrieval is a significant and challenging field of AI that focuses on finding and analyzing legal norms and documents relevant to a user’s information needs.
The objective of this article is to identify and synthesize the main approaches, trends, and advances in the application of AI in Legal Information Retrieval. Through a review of recent research, this study aims to provide a clear overview of the strategies used, methodologies employed, and emerging areas of focus.
To achieve these objectives, exhaustive search methods were applied to academic databases and repositories, selecting relevant studies published over the past twelve years. In the first stage, 2307 articles were found. Then, in the second stage, 354 technical articles were selected after applying inclusion/exclusion criteria. Finally, after a third round of filtering, 18 articles were selected for final analysis. In addition to the inclusion-exclusion criteria, the articles underwent a process of analysis and classification based on themes, methods, and results.
The findings reflect a growing interest in the application of AI techniques in Legal Information Retrieval. The approaches identified focus on improving the relevance of search results and automating legal processes. There is also a progressive adoption of Natural Language Processing and machine learning techniques.
Finally, a panoramic view is provided of the intersection between AI and Legal Information Retrieval. The results highlight the importance and potential of AI techniques in the legal field, while underscoring the need for deeper research and integrated approaches to address the specific challenges of Legal Information Retrieval in a technologically dynamic world.
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References
O. M. Spositto, V. Ledesma, G. Procopio, and J. Bossero, “Inteligencia artificial aplicada al Poder judicial,” in XXII Workshop de Investigadores en Ciencias de la Computación (WICC 2020, El Calafate, Santa Cruz).page, 2020, pp. 7–11. Accessed: Jun. 26, 2023. [Online]. Available: http://sedici.unlp.edu.ar/handle/10915/103381
J. Catacora, A. Casali, and C. Deco, “Legal Information Retrieval System with Entity-Based Query Expansion: Case study in Traffic Accident Litigation,” J Comput Sci Technol, vol. 22, no. 2, pp. e12–e12, 2022, Accessed: Jun. 18, 2023. [Online]. Available: http://sedici.unlp.edu.ar/handle/10915/146929
M. Van Opijnen and C. Santos, “On the concept of relevance in legal information retrieval,” Artif Intell Law (Dordr), vol. 25, no. 1, pp. 65–87, 2017, doi: 10.1007/s10506-017-9195-8.
D. Xu and C. Ruan, “Modern Theoretical Tools for Understanding and Designing Next-Generation Information Retrieval System,” in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, in WSDM ’22. New York, NY, USA: Association for Computing Machinery, 2022, pp. 1635–1637. doi: 10.1145/3488560.3501394.
P. Brereton, B. A. Kitchenham, D. Budgen, M. Turner, and M. Khalil, “Lessons from applying the systematic literature review process within the software engineering domain,” Journal of Systems and Software, vol. 80, no. 4, pp. 571–583, Apr. 2007, doi: 10.1016/J.JSS.2006.07.009.
S. Yassine, M. Esghir, and O. Ibrihich, “Using Artificial Intelligence Tools in the Judicial Domain and the Evaluation of their Impact on the Prediction of Judgments,” Procedia Comput Sci, vol. 220, pp. 1021–1026, Jan. 2023, doi: 10.1016/J.PROCS.2023.03.142.
D. Chakrabarti et al., “Use of Artificial Intelligence to Analyse Risk in Legal Documents for a Better Decision Support,” in TENCON 2018 - 2018 IEEE Region 10 Conference, 2018, pp. 683–688. doi: 10.1109/TENCON.2018.8650382.
F. de Arriba-Pérez, S. García-Méndez, F. J. González-Castaño, and J. González-González, “Explainable machine learning multi-label classification of Spanish legal judgements,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 10180–10192, Nov. 2022, doi: 10.1016/J.JKSUCI.2022.10.015.
L. V Naykhanova and I. V Naykhanova, “Recognition of Situations Described in the Text of Legal Documents,” in 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), 2019, pp. 1–4. doi: 10.1109/FarEastCon.2019.8934044.
E. Mumcuoğlu, C. E. Öztürk, H. M. Ozaktas, and A. Koç, “Natural language processing in law: Prediction of outcomes in the higher courts of Turkey,” Inf Process Manag, vol. 58, no. 5, p. 102684, Sep. 2021, doi: 10.1016/J.IPM.2021.102684.
W. Yuan, “Design and Implementation of Intelligent Reasoning Engine Based on Legal Framework Network Database,” in 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 2022, pp. 1000–1003. doi: 10.1109/ICSSIT53264.2022.9716419.
P. Poudyal, “A Machine Learning Approach to Argument Mining in Legal Documents,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10791, pp. 443–450, 2018, doi: 10.1007/978-3-030-00178-0_30.
Rupali Sunil Wagh, “Knowledge Discovery from Legal Documents Dataset using Text Mining Techniques,” in International Journal of Computer Applications (0975 – 8887) Volume 66– No.23, March 2013, 2013, pp. 448–455. doi: DOI: 10.5120/11258-6501.
C. Carpineto and G. Romano, “A Survey of Automatic Query Expansion in Information Retrieval,” ACM Comput. Surv., vol. 44, no. 1, Jan. 2012, doi: 10.1145/2071389.2071390.
I. Chalkidis, C. Nikolaou, P. Soursos, and M. Koubarakis, “Modeling and Querying Greek Legislation Using Semantic Web Technologies,” in The Semantic Web, D. and G. A. and H. R. and H. P. and H. O. Blomqvist Eva and Maynard, Ed., Cham: Springer International Publishing, 2017, pp. 591–606. doi: 10.1007/978-3-319-58068-5_36.
C. M. de O. Rodrigues, F. L. G. de Freitas, E. F. S. Barreiros, R. R. de Azevedo, and A. T. de Almeida Filho, “Legal ontologies over time: A systematic mapping study,” Expert Syst Appl, vol. 130, pp. 12–30, Sep. 2019, doi: 10.1016/J.ESWA.2019.04.009.
L. Yaqin, C. Gang, Z. Runkai, and S. Mengting, “Design of Contract Review System in Enterprise Legal Department Based on Natural Language Processing,” in 2020 15th International Conference on Computer Science & Education (ICCSE), 2020, pp. 331–335. doi: 10.1109/ICCSE49874.2020.9201618.
K. Atkinson, T. Bench-Capon, and D. Bollegala, “Explanation in AI and law: Past, present and future,” Artif Intell, vol. 289, p. 103387, Dec. 2020, doi: 10.1016/J.ARTINT.2020.103387.
T. Kabudi, I. Pappas, and D. H. Olsen, “AI-enabled adaptive learning systems: A systematic mapping of the literature,” Computers and Education: Artificial Intelligence, vol. 2, p. 100017, 2021, doi: https://doi.org/10.1016/j.caeai.2021.100017.
S. Castano, M. Falduti, A. Ferrara, and S. Montanelli, “A knowledge-centered framework for exploration and retrieval of legal documents,” Inf Syst, vol. 106, p. 101842, May 2022, doi: 10.1016/J.IS.2021.101842.
Y. Lyu et al., “Improving legal judgment prediction through reinforced criminal element extraction,” Inf Process Manag, vol. 59, no. 1, p. 102780, Jan. 2022, doi: 10.1016/J.IPM.2021.102780.
Z. Pauzi and A. Capiluppi, “Applications of natural language processing in software traceability: A systematic mapping study,” Journal of Systems and Software, vol. 198, p. 111616, Apr. 2023, doi: 10.1016/J.JSS.2023.111616.
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2025-04-30
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Copyright (c) 2025 Juan DAlotto DAlotto, Claudia Pons, Leandro Antonelli
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How to Cite
[1]
“Artificial Intelligence Applied in Legal Information: A Systematic Mapping Study”, JCS&T, vol. 25, no. 1, p. e03, Apr. 2025, doi: 10.24215/16666038.25.e03.
