Papers by Natalia Semenova
Biomedical Entity Representation with Graph-Augmented Multi-Objective Transformer (2024.findings-naacl)
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| Challenge: | Modern biomedical concept representations are mostly trained on synonymous concept names from a biomedically knowledge base graph, ignoring the inter-concept interactions and a concept’s local neighborhood. |
| Approach: | They propose a Graph-Augmented Multi-Objective Transformer which captures both inter-concept and intra-conception interactions from the multilingual UMLS graph. |
| Outcome: | The proposed model captures inter- and intra-concept interactions from the multilingual UMLS graph using pre-trained language models and graph neural networks. |