Recognizing mentions of adverse drug reaction in social media using knowledge-infused recurrent models

Gabriel Stanovsky, Daniel Gruhl, Pablo N. Mendes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

54 Scopus citations

Abstract

Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are contextdependent and include long, varied and unconventional descriptions as compared to more formal medical symptom terminology. We use the CADEC corpus to train a recurrent neural network (RNN) transducer, integrated with knowledge graph embeddings of DBpedia, and show the resulting model to be highly accurate (93.4 F1). Furthermore, even when lacking high quality expert annotations, we show that by employing an active learning technique and using purpose built annotation tools, we can train the RNN to perform well (83.9 F1).

Original languageAmerican English
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages142-151
Number of pages10
ISBN (Electronic)9781510838604
DOIs
StatePublished - 2017
Externally publishedYes
Event15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017

Publication series

Name15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
Volume1

Conference

Conference15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
Country/TerritorySpain
CityValencia
Period3/04/177/04/17

Bibliographical note

Publisher Copyright:
© 2017 Association for Computational Linguistics.

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