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    <abstract>This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209.</abstract>
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%0 Conference Proceedings
%T Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings
%A Aroyehun, Segun Taofeek
%A Gelbukh, Alexander
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 aug
%I Association for Computational Linguistics
%C Florence, Italy
%F aroyehun-gelbukh-2019-detection
%X This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209.
%R 10.18653/v1/W19-3224
%U https://aclanthology.org/W19-3224
%U https://doi.org/10.18653/v1/W19-3224
%P 133-135

Markdown (Informal)

[Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings](https://aclanthology.org/W19-3224) (Aroyehun & Gelbukh, 2019)

• Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings (Aroyehun & Gelbukh, 2019)

ACL

• Segun Taofeek Aroyehun and Alexander Gelbukh. 2019. Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 133–135, Florence, Italy. Association for Computational Linguistics.