Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (3): 40-48.doi: 10.16088/j.issn.1001-6600.2021091503

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Identification of Adverse Drug Reaction on Social Media Using Bi-directional Language Model

LI Zhengguang1, CHEN Heng1*, LIN Hongfei2   

  1. 1. Research Center for Language Intelligence, Dalian University of Foreign Language, Dalian Liaoning 116044, China;
    2. School of Computer Science and Technology, Dalian University of Technology, Dalian Liaoning 116024, China
  • Received:2021-09-15 Revised:2021-12-20 Online:2022-05-25 Published:2022-05-27

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Abstract: More time-effective and wider adverse drug reactions are concealed in tweets related to feelings of taking medication. However, it is difficult to extract adverse drug reaction (ADR) from these tweets due to relatively shortness and sparseness of tweets. Therefore, a neural network model is proposed in this paper, which employes the pretrained bidirectional language model and attention mechanism to identify ADR. Firstly, specific character-level features are extracted via a pretrained bidirectional character-level neural language model. Secondly, the attention mechanism is used to capture local and global semantic contexts while extracting ADRs. Thirdly, to improve the efficiency of the proposed method, Character-level features are combined with word-level features. Finally, co-training is replaced with the pretrained of the whole-word level and fine-tuned pretrained character embeddings. These optimizations contribute to improving the performance of identification. The proposed model achieves better performance on the PSB 2016 Social Media Mining Sharing Task Workshop-Task 2: ADR Extraction, obtaining the F1-scores of 82.2% on official datasets. Character features are useful for distinguishing ADR and non-ADR in morphology. In addition, attention mechanism improves the performance of identifying ADR due to capturing local and global semantic contexts.

Key words: adverse drug reaction, social media, bi-directional language model, attention mechanism, pretrained model

CLC Number: 

  • TP391.1
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