Publications:

Weitzenboeck, E., Lison, P., Cyndecka, M. & Langford, M. (2022) GDPR and unstructured data: is anonymization possible? International Data Privacy Law, ipac008

Pilán, I., Lison, P, Øvrelid, L., Papadopoulou, A., Sánchez, D. & Batet, M. (2022) The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization. arXiv preprint arXiv:2202.00443.

Pierre Lison, Ildikó Pilán, David Sánchez, Montserrat Batet, and Lilja Øvrelid. 2021. Anonymisation Models for Text Data: State of the Art, Challenges and Future Directions. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 4188–4203, Online. Association for Computational Linguistics. [pdf]

Pierre Lison, Jeremy Barnes, and Aliaksandr Hubin. 2021. skweak: Weak supervision made easy for NLP. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 337–346, Online. Association for Computational Linguistics. [pdf] [code]