  | LIGA, D., & YU, L. (2025). Which Neurons Nudge Normative Stance? Causal Tests and Mechanistic Evidence via Contrastive Last-Token Steering. In Frontiers in Artificial Intelligence and Applications (pp. 110 - 120). IOS Press. doi:10.3233/faia251581  Peer reviewed |
  | KOCYIGIT, E., LIGA, D., & LENZINI, G. (2025). Augmenting Dark Patterns Text Data by Leveraging Large Language Models: A Multi-agent Framework and Parameter-Efficient Fine-Tuning. In Y. Mualla (Ed.), Advances in Explainability, Agents, and Large Language Models - 1st International Workshop on Causality, Agents and Large Models, CALM 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-3-031-89103-8_5 Peer reviewed |
ROBALDO, L., & LIGA, D. (2025). On the Interplay Between Entailments Among Obligations and their Violations. In Y. Nakano (Ed.), New Frontiers in Artificial Intelligence - JSAI International Symposium on Artificial Intelligence, JSAI-isAI 2025, Proceedings. Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-981-96-7071-0_2 Editorial reviewed |
Kanwal, S., ROBALDO, L., & LIGA, D. (2025). Leveraging LLMs and LegalDocML to extract legal interpretations: a case study on UK legislation and case law [Paper presentation]. JURISIN 2025. Peer reviewed |
KANWAL, S., ROBALDO, L., Dar Hafsa, LIGA, D., & Anim Joseph. (2025). Foundations for Territorial Disambiguation in Law: A Preliminary Study using the Education Act 2005 [Paper presentation]. Jurisin 2025. Peer reviewed |
Yazan Mualla, Liuwen Yu, LIGA, D., TCHAPPI HAMAN, I., & MARKOVICH, R. (2025). Advances in Explainability, Agents, and Large Language Models. Springer Nature Switzerland. doi:10.1007/978-3-031-89103-8 Peer reviewed |
LIGA, D. (2024). An example of Argumentation Scheme from Liability: the case of Vicarious Liability. In New Frontiers in Artificial Intelligence. JSAI-isAI 2023. Lecture Notes in Computer Science. Switzerland: Springer. doi:10.1007/978-3-031-60511-6_9 Peer reviewed |
LIGA, D. (2024). Using Ontological Knowledge and Large Language Model Vector Similarities to Extract Relevant Concepts in VAT-Related Legal Judgments. In New Frontiers in Artificial Intelligence. JSAI-isAI 2023. Lecture Notes in Computer Science. Switzerland: Springer. doi:10.1007/978-3-031-60511-6_8 Peer reviewed |
 | LIGA, D. (2024). Argumentative Sequence Labelling Using Transfer Learning. In F. Paglieri, A. Ansani, ... M. Marini (Eds.), The Cognitive Dimension of Social Argumentation. London: College Publications. Editorial reviewed |
LIGA, D., & ROBALDO, L. (November 2023). Fine-tuning GPT-3 for legal rule classification. Computer Law & Security Review, 51, 105864. doi:10.1016/j.clsr.2023.105864 Editorial reviewed |
 | LIGA, D., MARKOVICH, R., & Amitrano, D. (2023). PaTrOnto, an ontology for patents and trademarks [Paper presentation]. JURISIN 2023, Kumamoto, Japan. Peer reviewed |
LIGA, D., Bentzen, B., Liao, B., MARKOVICH, R., Xiong, M., Wei, B., & Xu, T. (Eds.). (2023). Logics for AI and Law: Joint Proceedings of the Third International Workshop on Logics for New-Generation Artificial Intelligence and the International Workshop on Logic, AI and Law, September 8-9 and 11-12, 2023, Hangzhou. Hangzhou, China: College Publications. Editorial reviewed |
 | LIGA, D. (2023). The Interplay Between Lawfulness and Explainability in the Automated Decisionmaking of the EU Administration. SSRN Electronic Journal. doi:10.2139/ssrn.4561012 Peer reviewed |
LIGA, D., & PASETTO, L. (2023). Testing the reasoning of Large Language Models on tic-tac-toe [Paper presentation]. ACLAI 2023. Peer reviewed |
 | LIGA, D., MARKOVICH, R., & Fidelangeli, A. (2023). OntoVAT, an ontology for knowledge extraction in VAT-related judgments [Paper presentation]. JURISIN 2023, Kumamoto, Japan. Peer reviewed |
 | LIGA, D., & PASETTO, L. (2023). Testing spatial reasoning of Large Language Models: the case of tic-tac-toe [Paper presentation]. AIxPAC, Rome, Italy. Peer reviewed |
 | LIGA, D. (2023). The Argumentation Scheme from Vicarious Liability [Paper presentation]. JURISIN 2023, Kumamoto, Japan. Peer reviewed |
 | LIGA, D., & Palmirani, M. (2022). Deontic Sentence Classification Using Tree Kernel Classifiers. In Intelligent Systems and Applications (pp. 54-73). Springer, Cham. Peer reviewed |
 | LIGA, D. (2022). Hybrid Artificial Intelligence to extract patterns and rules from argumentative and legal texts [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/52142 |
 | LIGA, D., & Palmirani, M. (2022). Transfer Learning for Deontic Rule Classification: the Case Study of the GDPR. In INTERNATIONAL CONFERENCE ON LEGAL KNOWLEDGE AND INFORMATION SYSTEMS, Saarbrücken 14-16 December 2022. EasyChair. Peer reviewed |
 | Palmirani, M., & LIGA, D. (2022). Derogations Analysis of European Legislation Through Hybrid AI Approach. In Electronic Government and the Information Systems Perspective: 11th International Conference, EGOVIS 2022 (pp. 123-137). Springer, Cham. Peer reviewed |
 | Palmirani, M., Sovrano, F., LIGA, D., Sapienza, S., & Vitali, F. (2021). Hybrid AI Framework for Legal Analysis of the EU Legislation Corrigenda. In Legal Knowledge and Information Systems (pp. 68-75). IOS Press. doi:10.3233/FAIA210319 Peer reviewed |
 | LIGA, D., & Palmirani, M. (2020). Combining tree kernels and tree representations to classify argumentative stances. In Advances in Semantics and Linked Data: Joint Workshop Proceedings from ISWC 2020 (pp. 12-23). Peer reviewed |
 | LIGA, D., & Palmirani, M. (2020). Uncertainty in Argumentation Schemes: Negative Consequences and Basic Slippery Slope. In Logic and Argumentation (pp. 259-278). Springer, Cham. doi:10.1007/978-3-030-44638-3_16 Peer reviewed |
 | LIGA, D., & Palmirani, M. (2020). Transfer Learning with Sentence Embeddings for Argumentative Evidence Classification. In Proceedings of the 20th Workshop on Computational Models of Natural Argument (pp. 11). CEUR-WS.org Sun SITE Central Europe. Peer reviewed |
 | LIGA, D., & Palmirani, M. (2020). Argumentation Schemes as Templates? Combining Bottom-up and Top-down Knowledge Representation. In Proceedings of the 20th Workshop on Computational Models of Natural Argument (pp. 51). 56. Peer reviewed |
 | LIGA, D., & Palmirani, M. (2019). Classifying argumentative stances of opposition using Tree Kernels. In ACAI 2019: Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. doi:10.1145/3377713.3377717 Peer reviewed |
 | LIGA, D., & Palmirani, M. (2019). Detecting “slippery slope” and other argumentative stances of opposition using tree kernels in monologic discourse. In International Joint Conference on Rules and Reasoning (pp. 180-189). Springer, Cham. doi:10.1007/978-3-030-31095-0_13 Peer reviewed |
 | LIGA, D. (2019). Argumentative evidences classification and argument scheme detection using tree kernels. In Proceedings of the 6th Workshop on Argument Mining (pp. 92-97). doi:10.18653/v1/W19-4511 Peer reviewed |
 | LIGA, D. (2019). Comparing Tree Kernels performances in argumentative evidence classification. In CLADAG 2019 - Book of Short Papers. Peer reviewed |
Ruoto, A., Santarcangelo, V., LIGA, D., Oddo, G., Giacalone, M., & Iorio, E. (2017). The Sentiment of the infosphere: a sentiment analysis approach for the big conversation on the net. In Data science and social research (pp. 215-222). Springer, Cham. doi:10.1007/978-3-319-55477-8_20 Peer reviewed |
