[2604.06424] Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking

Computer Science > Computation and Language arXiv:2604.06424 (cs) [Submitted on 7 Apr 2026] Title:Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking Authors:Georgi Grazhdanski, Sylvia Vassileva, Ivan Koychev, Svetla Boytcheva View a PDF of the paper titled Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking, by Georgi Grazhdanski and 3 other authors View PDF HTML (experimental) Abstract:This paper presents a transformer-based approach to solving the SympTEMIST named entity recognition (NER) and entity linking (EL) tasks. For NER, we fine-tune a RoBERTa-based (1) token-level classifier with BiLSTM and CRF layers on an augmented train set. Entity linking is performed by generating candidates using the cross-lingual SapBERT XLMR-Large (2), and calculating cosine similarity against a knowledge base. The choice of knowledge base proves to have the highest impact on model accuracy. Comments: Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.06424 [cs.CL]   (or arXiv:2604.06424v1 [cs.CL] for this version)   https://doi.org/10.48550/arXiv.2604.06424 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.5281/zenodo.10103749 Focus to learn more DOI(s) linking to related resources Submission history From: Sylvia Vassileva [view email] [v1] Tue, 7 Apr 2026 20:00:59 UTC (8 KB) Full-text link...