Detection of adverse drug reactions in post-approval periods is a crucial challenge for pharmacology. Social media and electronic clinical reports arc becoming increasingly popular as a source for obtaining health-related information. In this work, we focus on extraction information of adverse drug reactions from various sources of biomedical text-based information, including biomedical literature and social media. We formulate the problem as a binary classification task and compare the performance of four state-of-the-art attention-based neural networks in terms of the F-measure. We show the effectiveness of these methods on four different benchmarks.
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