Abstract
The management of legal documents, especially court judgments, can be a daunting task due to the vast amounts of data involved. Traditional methods of managing legal documents are no longer sufficient, as the volume of data continues to increase, leading to the need for more advanced and efficient systems. The proposed infrastructure seeks to address this challenge by organizing a repository of textual documents and annotating them in a way that facilitates various downstream tasks. The framework is designed to be developed and maintained in a sustainable way, ensuring multiple services and uses of the annotated document repository while considering the limited availability of annotated data. This approach ensures that the output of the annotation algorithms aligns with the organizational processes used in Italian courts. The experiments conducted to demonstrate the feasibility of the solution employed different low-resource methods and solutions designed to combine these approaches in a meaningful way.
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References
Breit, A., Waltersdorfer, L., Ekaputra, F.J., Sabou, M.: An architecture for extracting key elements from legal permits. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 2105–2110 (2020). https://doi.org/10.1109/BigData50022.2020.9378375
Amato, F., Mazzeo, A., Penta, A., Picariello, A.: Using NLP and ontologies for notary document management systems. In: Database and Expert Systems Application, DEXA 2008, pp. 67–71 (2008). https://doi.org/10.1109/DEXA.2008.86
Buey, M.G., Garrido, A.L., Bobed, C., Ilarri, S.: The AIS project: boosting information extraction from legal documents by using ontologies. In: Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016), pp. 438–445 (2016). https://doi.org/10.5220/0005757204380445
Ruiz, M., Roman, C., Garrido, A.L., Mena, E.: uAIS: an experience of increasing performance of NLP information extraction tasks from legal documents in an electronic document management system. In: Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020), pp. 189–196 (2020). https://doi.org/10.5220/0009421201890196
Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., Sun, M.: How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence, arXiv, cs.2004.12158 (2020)
Rabelo, J., Goebel, R., Kim, M. Y., Kano, Y., Yoshioka, M., Satoh, K.: Overview and discussion of the competition on legal information extraction/entailment (COLIEE) 2021. Review Socionetwork Strategies 16, 111–133 (2022). https://doi.org/10.1007/s12626-022-00105-z
Yu, D., Huang, L., Ji, H.: Open relation extraction and grounding. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 854–864 (2017). https://aclanthology.org/I17-1086
Eberts, M., Ulges, A.: Span-based joint entity and relation extraction with transformer pre-training. Front. Artif. Intell. Appl. 325, 2006–2013 (2020). ECAI 2020
Dragoni, Mauro, Villata, Serena, Rizzi, Williams, Governatori, Guido: Combining natural language processing approaches for rule extraction from legal documents. In: Pagallo, Ugo, Palmirani, Monica, Casanovas, Pompeu, Sartor, Giovanni, Villata, Serena (eds.) AICOL 2015-2017. LNCS (LNAI), vol. 10791, pp. 287–300. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00178-0_19
Gormley, C., Tong, Z.: Elasticsearch: The Definitive Guide: A Distributed Real-Time Search and Analytics Engine. O’Reilly Media Inc, Sebastopol (2015)
Anisetti, M., Ardagna, C.A., Braghin, C., Damiani, E., Polimeno, A., Balestrucci, A.: Dynamic and scalable enforcement of access control policies for big data. In: Proceedings of the 13th International Conference on Management of Digital EcoSystems (MEDES 2021), pp. 71–78. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3444757.3485107
Chang, M.-W., Ratinov, L.-A., Roth, D., Srikumar, V.: Importance of semantic representation: dataless classification. In: AAAI, vol. 2, pp. 830–835 (2008)
Reimers, N., Gurevych, I.: Sentence-bert: sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019)
Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Androutsopoulos, I.: Large-scale multi-label text classification on EU legislation. arXiv preprint arXiv:1906.02192 (2019)
Chalkidis, I., et al.: LEGAL-BERT: the muppets straight out of law school. arXiv preprint arXiv:2010.02559 (2020)
Batini, C., Bellandi, V., Ceravolo, P., Moiraghi, F., Palmonari, M., Siccardi, S.: Semantic data integration for investigations: lessons learned and open challenges. In: 2021 IEEE International Conference on Smart Data Services (SMDS), Chicago, IL, USA, pp. 173–183 (2021). https://doi.org/10.1109/SMDS53860.2021.00031
Licari, D., Comandè, G.: ITALIAN-LEGAL-BERT: a pre-trained transformer language model for Italian law. In: CEUR Workshop Proceedings, vol. 3256. CEUR-WS (2022)
Bhattacharya, P., et al.: FIRE 2019 AILA track: artificial intelligence for legal assistance. In: Proceedings of the 11th Annual Meeting of the Forum for Information Retrieval Evaluation (2019)
Ardagna, C.A., Bellandi, V., Bezzi, M., Ceravolo, P., Damiani, E., Hebert, C.: Model-based big data analytics-as-a-service: take big data to the next level. IEEE Transactions Services Computing 14(2), 516–529 (2021). https://doi.org/10.1109/TSC.2018.2816941
Grootendorst, M.: BERTopic: neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794 (2022)
Bellandi, V., et al.: Knowledge-based legal document retrieval: a case study on Italian civil court decisions. In: Proceedings of the 1st International Knowledge Management for Law Workshop (KM4LAW), Bozen-Bolzano, Italy. CEUR-WS (2022)
Bellandi, V., Siccardi, S.: An Entity Registry: A Model for a Repository of Entities Found in a Document Set, pp. 1–12 (2023). https://doi.org/10.5121/csit.2023.130301
Carmignani, A., Giacomelli, S.: Too many lawyers? Litigation in Italian civil courts. Bank of Italy, Economic Research and International Relations Area (2010). https://ideas.repec.org/s/bdi/wptemi.html
Acknowledgements
This work is partially supported by i) the Next Generation UPP project within the PON programme of the Italian Ministry of Justice, ii) the Università degli Studi di Milano within the program “Piano di sostegno alla ricerca”, iii) the MUSA - Multilayered Urban Sustainability Action - project, funded by the European Union - NextGenerationEU, under the National Recovery and Resilience Plan (NRRP) Mission 4 Component 2 Investment Line 1.5: Strenghtening of research structures and creation of R &D “innovation ecosystems”, set up of “territorial leaders in R &D, and iv) the project SERICS (PE00000014) under the MUR NRRP funded by the EU - NextGenerationEU.
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Bellandi, V., Castano, S., Montanelli, S., Riva, D., Siccardi, S. (2024). A Service Infrastructure for the Italian Digital Justice. In: Chbeir, R., Benslimane, D., Zervakis, M., Manolopoulos, Y., Ngyuen, N.T., Tekli, J. (eds) Management of Digital EcoSystems. MEDES 2023. Communications in Computer and Information Science, vol 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-51643-6_13
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