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arXiv · 2604.06424

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

Georgi Grazhdanski·Sylvia Vassileva·Ivan Koychev·Svetla Boytcheva
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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.

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Georgi Grazhdanski, Sylvia Vassileva, Ivan Koychev, Svetla Boytcheva. 2026-04-07. Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking. https://doi.org/10.5281/zenodo.10103749

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