MedLinker: Medical Entity Linking with Neural Representations and Dictionary Matching

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Loureiro, D (Author) Other; Jorge, AM (Author) FCUP

Conference proceedings International

Title: Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part II

Pages: 230-237

42nd European Conference on IR Research, ECIR 2020

14 April 2020 through 17 April 2020

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-00S-0PE

DOI: 10.1007/978-3-030-45442-5_29

Abstract (EN): Progress in the field of Natural Language Processing (NLP) has been closely followed by applications in the medical domain. Recent advancements in Neural Language Models (NLMs) have transformed the field and are currently motivating numerous works exploring their application in different domains. In this paper, we explore how NLMs can be used for Medical Entity Linking with the recently introduced MedMentions dataset, which presents two major challenges: (1) a large target ontology of over 2M concepts, and (2) low overlap between concepts in train, validation and test sets. We introduce a solution, MedLinker, that addresses these issues by leveraging specialized NLMs with Approximate Dictionary Matching, and show that it performs competitively on semantic type linking, while improving the state-of-the-art on the more fine-grained task of concept linking (+4 F1 on MedMentions main task). © Springer Nature Switzerland AG 2020.

Language: English

Type (Professor's evaluation): Scientific