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        <title>Clinical Concept Linking with Contextualized Neural Representations</title>
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        <namePart type="given">Elliot</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>In traditional approaches to entity linking, linking decisions are based on three sources of information – the similarity of the mention string to an entity’s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.</abstract>
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%0 Conference Proceedings
%T Clinical Concept Linking with Contextualized Neural Representations
%A Schumacher, Elliot
%A Mulyar, Andriy
%A Dredze, Mark
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F schumacher-etal-2020-clinical
%X In traditional approaches to entity linking, linking decisions are based on three sources of information – the similarity of the mention string to an entity’s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.
%R 10.18653/v1/2020.acl-main.760
%U https://aclanthology.org/2020.acl-main.760
%U https://doi.org/10.18653/v1/2020.acl-main.760
%P 8585-8592

Markdown (Informal)

[Clinical Concept Linking with Contextualized Neural Representations](https://aclanthology.org/2020.acl-main.760) (Schumacher et al., ACL 2020)

• Clinical Concept Linking with Contextualized Neural Representations (Schumacher et al., ACL 2020)

ACL

• Elliot Schumacher, Andriy Mulyar, and Mark Dredze. 2020. Clinical Concept Linking with Contextualized Neural Representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8585–8592, Online. Association for Computational Linguistics.