Abstract
In this study, we propose a Legal Document Retrieval Pipeline. Given a legal case, we construct a scenario retrieval process based on various types of Essential Elements for Prosecution (EEP) associated with different criminal charges. We employ a reading comprehension model to extract essential scenario details, meeting the requirements of individual criminal charges. Subsequently, we extract keywords from these essential scenarios and utilize the embeddings of these keywords to compute the cosine similarity between each essential element, thus identifying the most closely related judgment documents. This approach dissects the overall direction of judgments into smaller components and derives similar judgments by matching the details within the judgment documents. In this study, we use the crimes of forgery and breach of trust as preliminary case types. We incorporate ChatGPT to assess the similarity between two judicial documents. We demonstrate that ChatGPT’s similarity judgments closely align with those of legal experts. The experiment results demonstrate the effectiveness of our legal document retrieval pipeline.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barmakian, D.: Better search engines for law. Law Libr. J. 92, 399 (2000)
Gao, J., et al.: FIRE2019@AILA: legal retrieval based on information retrieval model. In: FIRE (Working Notes), pp. 64–69 (2019)
Geist, A.: Using citation analysis techniques for computer-assisted legal research in continental jurisdictions. Available at SSRN 1397674 (2009)
Grimmelmann, J.: The structure of search engine law. Iowa L. Rev. 93, 1 (2007)
Grootendorst, M.: KeyBERT: minimal keyword extraction with BERT (2020). https://doi.org/10.5281/zenodo.4461265
Hu, W., et al.: BERT_LF: a similar case retrieval method based on legal facts. Wirel. Commun. Mob. Comput. 2022 (2022)
Li, H., et al.: Sailer: structure-aware pre-trained language model for legal case retrieval. arXiv preprint arXiv:2304.11370 (2023)
Ma, Y., et al.: Lecard: a legal case retrieval dataset for Chinese law system. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2342–2348 (2021)
Maxwell, K.T., Schafer, B.: Concept and context in legal information retrieval. In: Legal Knowledge and Information Systems, pp. 63–72. IOS Press (2008)
Ming, X.: text2vec: a tool for text to vector (2022). https://github.com/shibing624/text2vec
Rossi, J., Kanoulas, E.: Legal information retrieval with generalized language models. In: Proceedings of the 6th Competition on Legal Information Extraction/Entailment. COLIEE (2019)
Shao, Y., et al.: BERT-PLI: modeling paragraph-level interactions for legal case retrieval. In: IJCAI, pp. 3501–3507 (2020)
Turtle, H.: Text retrieval in the legal world. Artif. Intell. Law 3, 5–54 (1995)
Van Opijnen, M., Santos, C.: On the concept of relevance in legal information retrieval. Artif. Intell. Law 25, 65–87 (2017)
Vuong, Y.T.H., et al.: SM-BERT-CR: a deep learning approach for case law retrieval with supporting model. Artif. Intell. Law 1–28 (2022)
Wehnert, S., Sudhi, V., Dureja, S., Kutty, L., Shahania, S., De Luca, E.W.: Legal norm retrieval with variations of the BERT model combined with TF-IDF vectorization. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pp. 285–294 (2021)
Yu, W., et al.: Explainable legal case matching via inverse optimal transport-based rationale extraction. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 657–668 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, CH., Wang, CH., Fan, YC., Leu, FY. (2024). Legal Case Retrieval by Essential Element Extraction Based on Reading Comprehension Model. In: Barolli, L. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 214. Springer, Cham. https://doi.org/10.1007/978-3-031-64766-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-031-64766-6_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-64765-9
Online ISBN: 978-3-031-64766-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)