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        <title>Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models</title>
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        <namePart type="given">Elena</namePart>
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    <abstract>Linking of biomedical entity mentions to various terminologies of chemicals, diseases, genes, adverse drug reactions is a challenging task, often requiring non-syntactic interpretation. A large number of biomedical corpora and state-of-the-art models have been introduced in the past five years. However, there are no general guidelines regarding the evaluation of models on these corpora in single- and cross-terminology settings. In this work, we perform a comparative evaluation of various benchmarks and study the efficiency of state-of-the-art neural architectures based on Bidirectional Encoder Representations from Transformers (BERT) for linking of three entity types across three domains: research abstracts, drug labels, and user-generated texts on drug therapy in English. We have made the source code and results available at https://github.com/insilicomedicine/Fair-Evaluation-BERT.</abstract>
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%0 Conference Proceedings
%T Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models
%A Tutubalina, Elena
%A Kadurin, Artur
%A Miftahutdinov, Zulfat
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F tutubalina-etal-2020-fair
%X Linking of biomedical entity mentions to various terminologies of chemicals, diseases, genes, adverse drug reactions is a challenging task, often requiring non-syntactic interpretation. A large number of biomedical corpora and state-of-the-art models have been introduced in the past five years. However, there are no general guidelines regarding the evaluation of models on these corpora in single- and cross-terminology settings. In this work, we perform a comparative evaluation of various benchmarks and study the efficiency of state-of-the-art neural architectures based on Bidirectional Encoder Representations from Transformers (BERT) for linking of three entity types across three domains: research abstracts, drug labels, and user-generated texts on drug therapy in English. We have made the source code and results available at https://github.com/insilicomedicine/Fair-Evaluation-BERT.
%R 10.18653/v1/2020.coling-main.588
%U https://aclanthology.org/2020.coling-main.588
%U https://doi.org/10.18653/v1/2020.coling-main.588
%P 6710-6716

Markdown (Informal)

[Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models](https://aclanthology.org/2020.coling-main.588) (Tutubalina et al., COLING 2020)

• Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models (Tutubalina et al., COLING 2020)

ACL

• Elena Tutubalina, Artur Kadurin, and Zulfat Miftahutdinov. 2020. Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6710–6716, Barcelona, Spain (Online). International Committee on Computational Linguistics.