@inproceedings{stanovsky-etal-2017-recognizing,
title = "Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models",
author = "Stanovsky, Gabriel and
Gruhl, Daniel and
Mendes, Pablo",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1014/",
pages = "142--151",
abstract = "Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are context-dependent 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)."
}
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%0 Conference Proceedings
%T Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models
%A Stanovsky, Gabriel
%A Gruhl, Daniel
%A Mendes, Pablo
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F stanovsky-etal-2017-recognizing
%X Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are context-dependent 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).
%U https://aclanthology.org/E17-1014/
%P 142-151
Markdown (Informal)
[Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models](https://aclanthology.org/E17-1014/) (Stanovsky et al., EACL 2017)
- Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models (Stanovsky et al., EACL 2017)
ACL
- Gabriel Stanovsky, Daniel Gruhl, and Pablo Mendes. 2017. Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 142–151, Valencia, Spain. Association for Computational Linguistics.