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README.md
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license: cc-by-nc-nd-4.0
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---
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license: cc-by-nc-nd-4.0
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pipeline_tag: text-classification
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tags:
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- deep learning
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- law article retrieval
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- natural language processing
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- BERT
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- information retrieval
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- legal ai
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- legal bert
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- gdpr
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- general data protection regulation
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language:
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- en
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library_name: transformers
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---
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### Abstract
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The General Data Protection Regulation (GDPR) is an European regulation on data protection and privacy for all individuals within the European Union (EU) and the European Economic Area (EEA), and for all foreign subjects dealing with European citizens data. Therefore, the GDPR has important legislation implications that hold beyond EU member states. In this paper, we address the problem of GDPR article retrieval through the use of pre-trained language models (PLMs). Our approach features several key aspects, which include both domain-general and domain-specific pre-trained BERT models, further powered by self-supervised task-adaptive pre-training stages, with or without data enrichment based on recitals. Our study endeavors to demonstrate the potential of PLMs in addressing the challenges posed by the GDPR’s intricate legal framework, thus ultimately facilitating efficient access to GDPR provisions for government agencies, law firms, legal professionals, and citizens alike.
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### GDPR Article Retrieval based on Domain-adaptive and Task-adaptive Legal Pre-trained Language Models.
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### BibTeX Entry and Citation Info
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```
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@article{Lamberta,
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author = {Andrea Tagarelli and Andrea Simeri},
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title = {{Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code}},
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journal = {Artif. Intell. Law},
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volume = {30(3)},
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pages = {417--473. Published: 15 September 2021},
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year = {2022},
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doi ={10.1007/s10506-021-09301-8}
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}
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```
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### References
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- Tagarelli, A., Simeri, A. Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code. Artif Intell Law 30, 417–473 (2022). https://doi.org/10.1007/s10506-021-09301-8
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---
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