Name
Title | ||
|---|---|---|
Gated iterative capsule network for adverse drug reaction detection from social media |
Abstract | ||
|---|---|---|
In this paper, we propose a gated iterative capsule network model for the ADR detection task, named GICN. To alleviate the impact caused by abbreviations and misspelled words, we add character embedding as part of the input. Most ADRs consist of multiple words, e.g., short-term memory dysfunction. Hence, we apply a convolutional neural network (CNN) to obtain the complete phrase information. To effectively extract deep semantic information, we introduce a capsule network with a gated iteration unit that clusters features from underlying to high capsules. The gated iteration mechanism can remember contextual information, which will be introduced when clustering features. Experimental results show that our approach can achieve significant performance improvement for ADR detection from social media text compared with other state-of-the-art works. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BIBM49941.2020.9313092 | 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | DocType | ISBN |
adverse drug reactions,capsule network,gated iteration unit,social media | Conference | 978-1-7281-6216-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tongxuan Zhang | 1 | 3 | 2.44 |
| Hongfei Lin | 2 | 768 | 122.52 |
| Bo Xu | 3 | 4 | 4.77 |
| Yuqi Ren | 4 | 3 | 2.77 |
| Zhihao Yang | 5 | 73 | 15.35 |
| Jian Wang | 6 | 73 | 16.74 |
| Duan Xiaodong | 7 | 85 | 16.18 |