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Identifying Named Entities of Adverse Drug Reaction with Adversarial Transfer Learning |
Han Pu1,2( ),Zhong Yule1,Lu Haojie1,Ma Shiwen1 |
1School of Management, Nanjing University of Posts & Telecommunications, Nanjing 210003, China 2Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China |
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Abstract [Objective] This paper proposes an entity recognition model for adverse drug reactions based on adversarial transfer learning, ATL-BCA, aiming to address the problem of non-standard entity representations and insignificant boundaries in online health communities. [Methods] Firstly, we generated the external semantic feature vectors fused with the online medical domain knowledge with Word2Vec. Secondly, based on the transfer learning, we utilized the shared and private BiLSTM to extract the shared boundary information and private features for entity recognition and word segmentation tasks. Next, we used the multi-head attention mechanism to capture the overall sentence dependency and used adversarial training to filter the private information of the word segmentation task. This helped us eliminate the influence of redundant features on the entity recognition task. Finally, we predicted the label sequence results with the help of CRF constraints. [Results] We used a self-constructed social media adverse drug reaction dataset to examine the proposed model with. The F1 value of the new model reached 91.35%, which is 5.28% and 2.98% higher than Word2Vec-BiLSTM-CRF and BERT-BiLSTM-CRF. [Limitations] We only retrieved the experimental data from Sanjiu Health & Medicine Site, the scale of the constructed dataset is relatively small. [Conclusions] The ATL-BCA model fully utilizes the shared boundary information between entity recognition and word segmentation tasks. It also filters the private features of the word segmentation tasks, effectively improving the entity recognition performance of adverse drug reactions in online health communities.
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Received: 23 April 2022
Published: 13 April 2023
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Fund:National Social Science Fund of China(17CTQ022);Major Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province(2020SJZDA102);National Innovation Training Program for College Students(SZDG2021040) |
Corresponding Authors:
Han Pu,ORCID:0000-0001-5867-4292,E-mail:hanpu@niupt.edu.cn。
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