Triplet-Trained Vector Space and Sieve-Based Search Improve Biomedical Concept Normalization
AffiliationSchool of Information, University of Arizona
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CitationXu, D., & Bethard, S. (2021, June). Triplet-Trained Vector Space and Sieve-Based Search Improve Biomedical Concept Normalization. In Proceedings of the 20th Workshop on Biomedical Language Processing (pp. 11-22).
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AbstractConcept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is critical for mining and analyzing biomedical texts. We propose a vector-space model for concept normalization, where mentions and concepts are encoded via transformer networks that are trained via a triplet objective with online hard triplet mining. The transformer networks refine existing pre-trained models, and the online triplet mining makes training efficient even with hundreds of thousands of concepts by sampling training triples within each mini-batch. We introduce a variety of strategies for searching with the trained vector-space model, including approaches that incorporate domain-specific synonyms at search time with no model retraining. Across five datasets, our models that are trained only once on their corresponding ontologies are within 3 points of state-of-the-art models that are retrained for each new domain. Our models can also be trained for each domain, achieving new state-of-the-art on multiple datasets. © 2021 Association for Computational Linguistics
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Except where otherwise noted, this item's license is described as Copyright © 2021 Association for Computational Linguistics. Licensed on a Creative Commons Attribution 4.0 International License.