Clinical Concept Linking with Contextualized Neural Representations

Elliot Schumacher, Andriy Mulyar, Mark Dredze


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.
Anthology ID:
2020.acl-main.760
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8585–8592
Language:
URL:
https://aclanthology.org/2020.acl-main.760
DOI:
10.18653/v1/2020.acl-main.760
Bibkey:
Cite (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.
Cite (Informal):
Clinical Concept Linking with Contextualized Neural Representations (Schumacher et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.760.pdf
Video:
 http://slideslive.com/38929026