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 language||American English|
|Title of host publication||Long Papers|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||10|
|State||Published - 2017|
|Event||15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain|
Duration: 3 Apr 2017 → 7 Apr 2017
|Name||15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference|
|Conference||15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017|
|Period||3/04/17 → 7/04/17|
Bibliographical notePublisher Copyright:
© 2017 Association for Computational Linguistics.