Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking
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Abstract (arXiv)
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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References · 4
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2021Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linkingcited by 28
- 2021Biomedical and Clinical Language Models for Spanish: On the Benefits of Domain-Specific Pretraining in a Mid-Resource Scenariocited by 11
- 2021CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domaincited by 7
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