Papers by Sepideh Mesbah
Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content (D19-1)
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Sepideh Mesbah, Jie Yang, Robert-Jan Sips, Manuel Valle Torre, Christoph Lofi, Alessandro Bozzon, Geert-Jan Houben
| Challenge: | Existing dictionary-based, semi-supervised learning approaches are limited by the coverage and maintainability of laymen health vocabularies. |
| Approach: | They propose a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset and generate a small set of labeled training sets. |
| Outcome: | The proposed approach matches the performance of fully-supervised approaches while requiring only 25% of training data. |