Abstract
The use of machine learning and semantic analysis in case law is the new trend in modern society. Case Based Reasoning tools are being used to analyze texts in courts to make and predict judicial decisions which are designed to base the outcomes of current court proceedings from past and or learning from the mistakes to make better decisions. Because of the accuracy and speed of this technology, researchers in the justice system have introduced Machine Learning to optimize the Case-Based Researching approach. This paper presents a study aimed to critically analyze semantic analysis in the context of machine learning and proposes a case-based reasoning information retrieval system. It will explore how CBR-IR is being used to improve legal case law information retrieval. The study covers the importance of semantic analysis. The study will discuss limitations and recommendations for improvement and future research. The study recommends that it is necessary to conduct further research in semantic analysis and how they can be used to improve information retrieval of Canadian maritime case law.
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References
Venkateswarlu Naik, M.: Building a legal expert system for legal reasoning in specific domain-a survey. Int. J. Comput. Sci. Mach. Learn. 4(5), 175–184 (2012)
Mosweu T. L., Mosweu, O.: Electronic court records management systems: a review of literature in selected African countries. Mousaion: South African J. Info. Stud. 36(4) (2019)
Pratiwi, S.J., Steven, S., Permatasari, A.D.P.: The application of e-court as an effort to modernize the justice administration in indonesia: challenges & problems. Indonesian J. Adv. Legal Serv. 2(1), 39–56 (2020)
Imamnazarovna, N.: Aspects of legal regulation of electronic document and electronic document circulation in business. Am. J. Political Sci. Law Criminol. 02(11), 8–14 (2020)
Procopiuck, M.: Machine learning and time of judgment in specialized courts: What is the impact of changing from physical to electronic processing? Gov. Inf. Q. 35(3), 491–501 (2018)
Levitt, H.M., Morrill, Z., Collins, K.M., Rizo, J.L.: The methodological integrity of critical qualitative research: Principles to support design and research review. J. Couns. Psychol. 68(3), 357–370 (2021)
Epp, M., Otnes, C. C.: High-quality qualitative research: getting into gear. J. Serv. Res. 109467052096144 (2020)
Mészáros, P. E.: The evolution of electronic administration and its practice in judicial proceedings. Pravni vjesnik 34(3–4) (2018)
Hasan M. I., Mia, B.: Initiation of virtual court system during COVID-19 pandemic and e-judiciary: challenges and way forward. Daengku: J. Human. Soc. Sci. Innov. 1(1), 8–17 (2021)
Gupta, L., Gadiwala, S.: Coping with the Coronavirus Disease-2019 pandemic: a giant leap towards digital transformation in academic research. Indian J. Rheumatol. 16(2) (2021)
Tae, K., So, S.: Machine-learning-based deep semantic analysis approach for forecasting new technology convergence. Technol. Forecast. Soc. Change 157, 120095 (2020)
Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to make decisions of the European court of human rights. Artif. Intell. Law. 28, 237–266 (2019)
Huang, X., Zanni-Merk, C., Cremilleux, B.: Enhancing deep learning with semantics: an application to manufacturing time series analysis. Procedia Comput. Sci. 159, 437–446 (2019)
Sarker, H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2, 160 (2021)
Ebietomere, E.P., Ekuobese, G.O.: A semantic retrieval system for case law. Appl. Comput. Syst. 24(1), 38–48 (2019)
Cao, J., Wang, M., Li, Y., Zhang, Q.: Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment. PLoS ONE 14(4): e0215136 (2019)
Babacar, G., Dezheng, Z., Aziguli, W.: Improvement of support vector machine algorithm in big data background. Math. Probl. Eng. (2021). https://doi.org/10.1155/2021/5594899
Baoxian, J.: Application of intelligent information retrieval for big data oriented brain science. Adv. Eng. Res. 66 (2018)
Joby, D.: Expedient Information retrieval system for web pages using the natural language modeling. J. Artif. Intell. Cap. Netw. 2(2), 100–110 (2020)
Afuan, L., Ashari, A., Suyanto, Y.: A study: query expansion methods in information retrieval. J. Phys. Conf. Ser. 1367, 012001 (2019)
Tehseen, R.: Semantic Information retrieval: a survey. J. Inf. Technol. Softw. Eng. 08(04) (2018)
Lin, K.-S.: A case-based reasoning system for interior design using a new cosine similarity retrieval algorithm. J. Inf. Telecommun. 4(1), 91–104 (2020)
On-Piu Chan, J.: Digital transformation in the era of big data and cloud computing. Int. J. Intell. Inf. Syst. 9(3), 16 (2020)
Anaissi, A., Goyal, M., Catchpoole, D.R., Braytee, A., Kennedy, P.J.: Case-based retrieval framework for gene expression data. Cancer Inform. 14, 21–31 (2017)
Devi, M.U., Gandhi, G.M.: Scalable information retrieval system in semantic web by query expansion and ontological-based LSA ranking similarity measurement. Int. J. Adv. Intell. Paradig. 17(1/2), 44 (2020)
Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Mateo (1993)
Daniels, J. J., Rissland E. L.: A case-based approach to intelligent information retrieval. In: SIGIR'1995, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, Washington, USA, July 9–13 (1995) (Special Issue of the SIGIR Forum)
Feuillâtre, H., et al.: Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation. PLOS ONE (2020). https://doi.org/10.1371/journal.pone.0238463
Jian, W.: Design and implementation of campus network search engine based on Lucene. J. Hunan Inst. Eng. (2012)
Youzhuo, Z., Yu, F., Ruifeng, Z., Shuqing, H., Yi, W.: Research on Lucene based full-text query search service for smart distribution system (2020)
Acknowledgement
This research has been funded by the SUDOE Interreg Program -grant INUNDATIO-, by the Spanish Ministry of Economics and Industry, grant PID2020-112726RB-I00, by the Spanish Research Agency (AEI, Spain) under grant agreement RED2018–102312-T (IA-Biomed), and by the Ministry of Science and Innovation under CERVERA Excellence Network project CER-20211003 (IBERUS) and Missions Science and Innovation project MIG-20211008 (INMERBOT). Also, by Principado de Asturias, grant SV-PA-21-AYUD/2021/50994.
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Abimbola, B., Tan, Q., Villar, J.R. (2023). Introducing Intelligence to the Semantic Analysis of Canadian Maritime Case Law: Case Based Reasoning Approach. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_57
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