Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913013373599744 |
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| author | Grazhdanski, Georgi Vassileva, Sylvia Koychev, Ivan Boytcheva, Svetla |
| author_facet | Grazhdanski, Georgi Vassileva, Sylvia Koychev, Ivan Boytcheva, Svetla |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06424 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking Grazhdanski, Georgi Vassileva, Sylvia Koychev, Ivan Boytcheva, Svetla Computation and Language Artificial Intelligence 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. |
| title | Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.06424 |