NOBANI NAVID

De Santo, A., Nobani, N., Malandri, L., Mercorio, F., Mezzanzanica, M. (2026). Using language models to extract and map emerging skills to the ESCO taxonomy. DISCOVER ARTIFICIAL INTELLIGENCE [10.1007/s44163-026-01609-1]. Dettaglio

Nobani, N., Officioso, G., Pallucchini, F., Sperlì, G., Mercorio, F. (2026). Synthetic data generation: A tertiary study. INFORMATION PROCESSING & MANAGEMENT, 63(6 (September 2026)) [10.1016/j.ipm.2026.104715]. Dettaglio

D'Amico, S., Mercorio, F., Nobani, N., Sperlì, G., Ventre, C. (2026). Learning across modalities: a systematic survey of multimodal models for financial analysis. INFORMATION FUSION, 133(September 2026) [10.1016/j.inffus.2026.104249]. Dettaglio

Clerici, F., Nobani, N. (2026). Categorical variable encoding methods for tabular data: a benchmarking study. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 22(1) [10.1007/s41060-025-00886-w]. Dettaglio

De Santo, A., Malandri, L., Mercorio, F., Mezzanzanica, M., Nobani, N. (2026). SkiLLens: Recognising and Mapping Novel Skills from Millions of Job Ads Across Europe Using Language Models. In EACL 2026 - 19th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Vol. 1 - (Long Papers) (pp.877-885). Association for Computational Linguistics (ACL) [10.18653/v1/2026.eacl-industry.65]. Dettaglio