Papers by Artur Kadurin
Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models (2020.coling-main)
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| Challenge: | a large number of biomedical entity mentions are retrieved from different ontologies, requiring non-syntactic interpretation. |
| Approach: | They propose to use bidirectional encoder representations from transformers to link biomedical entities across three domains for a task called medical concept normalization. |
| Outcome: | The proposed neural architectures are efficient for linking biomedical entities across domains and corpora. |
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. |
Two Steps from Hell: Compositionality on Chemical LMs (2025.findings-emnlp)
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| Challenge: | Experiments with state-of-the-art ChemLLMs show significant performance drops in compositional tasks, highlighting the need for models that move beyond pattern recognition. |
| Approach: | They introduce a benchmark to evaluate chemical language models' understanding of chemical language by identifying and analyzing compositional patterns within chemical data. |
| Outcome: | The proposed benchmark shows that existing LLMs can handle complex queries without pattern recognition. |
Lost in Translation: Chemical Language Models and the Misunderstanding of Molecule Structures (2024.findings-emnlp)
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Veronika Ganeeva, Andrey Sakhovskiy, Kuzma Khrabrov, Andrey Savchenko, Artur Kadurin, Elena Tutubalina
| Challenge: | chemistry and natural language processing (NLP) have advanced drug discovery. |
| Approach: | They propose a framework for assessment of Chemistry LMs of different natures that relies on augmentations that preserve an underlying chemical. |
| Outcome: | The proposed framework relies on augmentations that preserve an underlying chemical, such as kekulization and cycle replacements. |