Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking
Computation and Language
2026-04-09 v1 Artificial Intelligence
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.
Keywords
Cite
@article{arxiv.2604.06424,
title = {Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking},
author = {Georgi Grazhdanski and Sylvia Vassileva and Ivan Koychev and Svetla Boytcheva},
journal= {arXiv preprint arXiv:2604.06424},
year = {2026}
}
Comments
6 pages, 3 tables, Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models, American Medical Informatics Association 2023 Annual Symposium