SpringerLink
Log in

A Rapid Semi-automated Literature Review on Legal Precedents Retrieval

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13566))

Included in the following conference series:

Abstract

Precedents constitute the starting point of judges’ reasoning in national legal systems. Precedents are also an essential input for case-based reasoning (CBR) methodologies. Although considerable research has been done on CBR applied to legal practice, the precedent retrieval techniques are a relatively new and unexplored field of AI & Law. Only a few works have tested or developed methods for identifying such previous similar cases. This work uses text mining (TM), natural language processing (NLP), and data visualization methods to provide a semi-automated rapid literature review and identify how justice courts and legal practitioners may use AI to retrieve similar cases. Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), automation techniques were used to expedite the literature review. In this study, we confirmed the feasibility of automation tools for expediting literature reviews and provided an overview of the current research state on legal precedents retrieval.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 42.79
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 53.49
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Functor is a function that maps elements of one set to those of another.

References

  1. Gomez, A.R.: Demand side justice. Georg. J. Poverty Law Policy XXVIII(3), 411–436 (2021)

    Google Scholar 

  2. Susskind, R.: The future of courts. Practice 6(5) (2020). https://thepractice.law.harvard.edu/article/the-future-of-courts/

  3. Guillaume, G.: The use of precedent by international judges and arbitrators. J. Int. Disput. Settl. 2(1), 5–23 (2011). https://doi.org/10.1093/JNLIDS/IDQ025

    Article  Google Scholar 

  4. Rigoni, A.: Common-law judicial reasoning and analogy. Leg. Theory 20(2), 133–156 (2014)

    Article  Google Scholar 

  5. Fon, V., Parisi, F.: Judicial precedents in civil law systems: a dynamic analysis. Int. Rev. Law Econ. 26(4), 519–535 (2006). https://doi.org/10.1016/j.irle.2007.01.005

    Article  Google Scholar 

  6. Kolodner, J.L.: An introduction to case-based reasoning. Artif. Intell. Rev. 6, 3–34 (1992)

    Google Scholar 

  7. Roth, A.: Case-based reasoning in the law: a formal theory of reasoning by case comparison. Universiteit Maastricht (2003)

    Google Scholar 

  8. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J. Clin. Epidemiol. 62(10), 1006–1012 (2009). https://doi.org/10.1016/j.jclinepi.2009.06.005

    Article  Google Scholar 

  9. Jahan, N., Naveed, S., Zeshan, M., Tahir, M.A.: How to conduct a systematic review: a narrative literature review (2016). https://doi.org/10.7759/cureus.864

  10. Garritty, C., et al.: Cochrane rapid reviews methods group offers evidence-informed guidance to conduct rapid reviews. J. Clin. Epidemiol. 130, 13–22 (2021). https://doi.org/10.1016/J.JCLINEPI.2020.10.007

    Article  Google Scholar 

  11. Tricco, A.C., Langlois, E.V., Straus, S.E.: Rapid reviews to strengthen health policy and systems: a practical guide. World Health Organization, p. 119 (2017)

    Google Scholar 

  12. Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., Moher, D.: Evidence summaries: the evolution of a rapid review approach. Syst. Rev. 1(1), 1–9 (2012). Accessed 12 Apr 2022. https://doi.org/10.1186/2046-4053-1-10

  13. Stevens, A., Garritty, C., Hersi, M., Moher, D.: Develo** PRISMA-RR, a reporting guideline for rapid reviews of primary studies (Protocol) (2018)

    Google Scholar 

  14. van Dinter, R., Tekinerdogan, B., Catal, C.: Automation of systematic literature reviews: a systematic literature review. Inf. Softw. Technol. 136, 106589 (2021). https://doi.org/10.1016/j.infsof.2021.106589

    Article  Google Scholar 

  15. Feng, L., Chiam, Y.K., Lo, S.K.: Text-mining techniques and tools for systematic literature reviews: a systematic literature review. In: Proceedings - Asia-Pacific Software Engineering Conference APSEC, vol. 2017-Decem, pp. 41–50 (2018). https://doi.org/10.1109/APSEC.2017.10

  16. Zimmerman, J., et al.: Iterative guided machine learning-assisted systematic literature reviews: a diabetes case study. Syst. Rev. 10(1) (2021). https://doi.org/10.1186/S13643-021-01640-6

  17. Moro, S., Cortez, P., Rita, P.: Business intelligence in banking: a literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Syst. Appl. 42(3), 1314–1324 (2015). https://doi.org/10.1016/j.eswa.2014.09.024

    Article  Google Scholar 

  18. António, N., de Almeida, A., Nunes, L.: Predictive models for hotel booking cancellation: a semi-automated analysis of the literature. Tour. Manag. Stud. 15(1), 7–21 (2019). https://doi.org/10.18089/tms.2019.15011

    Article  Google Scholar 

  19. Guerreiro, J., Rita, P., Trigueiros, D.: A text mining-based review of cause-related marketing literature. J. Bus. Ethics 139(1), 111–128 (2015). https://doi.org/10.1007/s10551-015-2622-4

    Article  Google Scholar 

  20. Self-defense|Wex|US Law|LII/Legal Information Institute. https://www.law.cornell.edu/wex/precedent. Accessed 12 Apr 2022

  21. Frampton, G.K., Livoreil, B., Petrokofsky, G.: Eligibility screening in evidence synthesis of environmental management topics. Environ. Evid. 6(1), 1–13 (2017). https://doi.org/10.1186/S13750-017-0102-2

  22. Asmussen, C.B., Møller, C.: Smart literature review: a practical topic modelling approach to exploratory literature review. J Big Data 6(1), 1–18 (2019). https://doi.org/10.1186/s40537-019-0255-7

    Article  Google Scholar 

  23. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003). https://doi.org/10.1016/b978-0-12-411519-4.00006-9

    Article  MATH  Google Scholar 

  24. O’callaghan, D., Greene, D., Carthy, J., Cunningham, P.: An analysis of the coherence of descriptors in topic modeling. Expert Syst. Appl. 42(13), 5645–5657 (2015)

    Article  Google Scholar 

  25. Arora, S., Ge, R., Moitra, A.: Learning topic models - going beyond SVD. In: Proceedings - Annual IEEE Symposium Foundation Computer Science, FOCS, pp. 1–10 (2012). https://doi.org/10.48550/arxiv.1204.1956

  26. Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retr. 3(4), 333–389 (2009). https://doi.org/10.1561/1500000019

    Article  Google Scholar 

  27. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2019, vol. 1, pp. 4171–4186 (2019). https://github.com/tensorflow/tensor2tensor

  28. Branting, L.K.: A reduction-graph model of precedent in legal analysis. Artif. Intell. 150(1–2), 59–95 (2003). https://doi.org/10.1016/S0004-3702(03)00102-4

    Article  MATH  Google Scholar 

  29. McLaren, B.M.: Extensionally defining principles and cases in ethics: an AI model. Artif. Intell. 150(1–2), 145–181 (2003). https://doi.org/10.1016/S0004-3702(03)00135-8

    Article  MATH  Google Scholar 

  30. Forbus, K.D., Gentner, D., Law, K.: MAC/FAC: a model of similarity-based retrieval. Cogn. Sci. 19, 141–205 (1994)

    Google Scholar 

  31. Liu, C.L., Chang, C.T., Ho, J.H.: Case instance generation and refinement for case-based criminal summary judgments in Chinese. J. Inf. Sci. Eng. 20(4), 783–800 (2004)

    Google Scholar 

  32. Wang, R., Zeng, Y.: Nonlinear nearest-neighbour matching and its application in legal precedent retrieval. In: Proceedings - 3rd International Conference Information Technology Applications ICITA 2005, vol. I, pp. 341–346 (2005)

    Google Scholar 

  33. Raman, V., Palanissamy, A.: Computer aided legal support system: an initial framework retrieving legal cases by case base reasoning approach. In: 2008 International Conference Innovation Information Technology IIT 2008, pp. 317–321 (2008). https://doi.org/10.1109/INNOVATIONS.2008.4781663

  34. Maxwell, K.T., Schafer, B.: Concept and context in legal information retrieval. Front. Artif. Intell. Appl. 189(1), 63–72 (2008). https://doi.org/10.3233/978-1-58603-952-3-63

    Article  Google Scholar 

  35. Kulkarni, Y.H., Patil, R., Shridharan, S.: Detection of catchphrases and precedence in legal documents. In: CEUR Workshop Proceedings, vol. 2036, pp. 86–89 (2017)

    Google Scholar 

  36. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: 31st International Conference on Machine Learning, ICML 2014, vol. 4, pp. 2931–2939 (2014)

    Google Scholar 

  37. Zhang, N., Pu, Y.F., Yang, S.Q., Zhou, J.L., Gao, J.K.: An ontological Chinese legal consultation system. IEEE Access 5, 18250–18261 (2017). https://doi.org/10.1109/ACCESS.2017.2745208

    Article  Google Scholar 

  38. Thuma, E., Motlogelwa, N.P.: On the importance of legal catchphrases in precedence retrieval. In: CEUR Workshop Proceedings, vol. 2036, pp. 92–94 (2017)

    Google Scholar 

  39. Nair, A.M., Wagh, R.S.: Similarity analysis of court judgements using association rule mining on case citation data-a case study. Int. J. Eng. Res. Technol. 11(3), 373–381 (2018)

    Google Scholar 

  40. Kiryu, Y., Ito, A., Kasahara, T., Hatano, H., Fujii, M.: A study of precedent retrieval system for civil trial. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2017. LNCS, vol. 10672, pp. 151–158. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74727-9_18

    Chapter  Google Scholar 

  41. Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: ICML Unsupervised Transfer Learning, pp. 37–50 (2012). https://doi.org/10.1561/2200000006

  42. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 2013 Proceedings of 26th International Conference on Neural Information Processing System, vol. 2, pp. 3111–3119 (2013)

    Google Scholar 

  43. Amin, K., Kapetanakis, S., Althoff, K.D., Dengel, A., Petridis, M.: Cases without borders: automating knowledge acquisition approach using deep autoencoders and siamese networks in case-based reasoning. In: Proceedings - International Conference Tools with Artificial Intelligence ICTAI, vol. 2019-Novem, pp. 133–140 (2019). https://doi.org/10.1109/ICTAI.2019.00027

  44. More, R., Patil, J., Palaskar, A., Pawde, A.: Removing named entities to find precedent legal cases. In: CEUR Workshop Proceedings, vol. 2517, no. December 2019, pp. 13–18 (2019)

    Google Scholar 

  45. Mansouri, A., Affendey, L.S., Mamat, A.: Named entity recognition approaches. J. Comput. Sci. 8(2), 339–344 (2008)

    Google Scholar 

  46. Bhattacharya, P., et al.: FIRE 2019 AILA track: artificial intelligence for legal assistance. In: ACM International Conference Proceeding Series, no. February 2018, pp. 4–6 (2019). https://doi.org/10.1145/3368567.3368587

  47. Di Nunzio, G.M.: A study on lemma vs stem for legal information retrieval using R tidyverse. IMS UniPD @ AILA 2020 Task 1. In: CEUR Workshop Proceedings, vol. 2826, pp. 54–59 (2020)

    Google Scholar 

  48. Angelov, D.: Top2Vec: distributed representations of topics, pp. 1–25 (2020). http://arxiv.org/abs/2008.09470

  49. Arora, J., Patankar, T., Shah, A., Joshi, S.: Artificial intelligence as legal research assistant. In: CEUR Workshop Proceedings, vol. 2826, no. December, pp. 60–65 (2020)

    Google Scholar 

  50. Mandal, A., Ghosh, K., Ghosh, S., Mandal, S.: Unsupervised approaches for measuring textual similarity between legal court case reports. Artif. Intell. Law 29(3), 417–451 (2021). https://doi.org/10.1007/s10506-020-09280-2

    Article  Google Scholar 

  51. Chalkidis, I.: Law2Vec: legal word embeddings (2018). https://archive.org/details/Law2Vec

Download references

Acknowledgments

Funding: This research was supported by a grant from the Portuguese Foundation for Science and Technology (“Fundação para a Ciência e a Tecnologia”) [grant number DSAIPA/DS/0116/2019].

Author information

Authors and Affiliations

  1. NOVA Information Management School, Lisbon, Portugal

    Hugo Silva, Nuno António & Fernando Bacao

Authors
  1. Hugo Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nuno António
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando Bacao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hugo Silva .

Editor information

Editors and Affiliations

  1. ISEP/GECAD, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  2. IST/INESC-ID, University of Lisbon, Lisbon, Portugal

    Bruno Martins

  3. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Ana Paiva

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Bernardete Ribeiro

  5. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Alberto Sardinha

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silva, H., António, N., Bacao, F. (2022). A Rapid Semi-automated Literature Review on Legal Precedents Retrieval. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16474-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16473-6

  • Online ISBN: 978-3-031-16474-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation