Abstract
Adverse drug reaction (ADR) is a major health concern. Identifying text that mentions ADRs from a large volume of social media data discussing other topics is a key preliminary but nontrivial task for drug-ADR pair detection. This task suffers from severe imbalance issue. Moreover, prior studies have overlooked the simultaneous use of high-level abstract information contained in data and the domain-specific information embedded in knowledge bases. Therefore, we propose a novel multi-view active learning approach, in which a selection strategy is tailored to the imbalanced dataset and various document representations are regarded as multi views. We capture data-driven and domain-specific information by resorting to deep learning methods and handcrafted feature engineering, respectively. Experimental results demonstrate the effectiveness of our proposed approach.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (Nos. 71701142 and 71971067), and China Postdoctoral Science Foundation (No. 2018M640346).
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Liu, J., Huang, L., Zhang, C. (2021). An Active Learning Approach for Identifying Adverse Drug Reaction-Related Text from Social Media Using Various Document Representations. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_1
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DOI: https://doi.org/10.1007/978-3-030-87571-8_1
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