Abstract
BackgroundDeep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Most CNN architectures incorporate a pooling layer to reduce the dimensionality of the convolution layer output, preserving relevant features and removing irrelevant details. All the previous CNN based systems for DDI extraction used max-pooling layers.ResultsIn this paper, we evaluate the performance of various pooling methods (in particular max-pooling, average-pooling and attentive pooling), as well as their combination, for the task of DDI extraction. Our experiments show that max-pooling exhibits a higher performance in F1-score (64.56%) than attentive pooling (59.92%) and than average-pooling (58.35%).ConclusionsMax-pooling outperforms the others alternatives because is the only one which is invariant to the special pad tokens that are appending to the shorter sentences known as padding. Actually, the combination of max-pooling and attentive pooling does not improve the performance as compared with the single max-pooling technique.
Highlights
Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction
Max-pooling outperforms the others alternatives because is the only one which is invariant to the special pad tokens that are appending to the shorter sentences known as padding
The combination of max-pooling and attentive pooling does not improve the performance as compared with the single max-pooling technique
Summary
Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Clinical trials are an essential phase of drug development process They aim to test the safety and effectiveness of new drugs, these studies are not able to capture all the possible adverse drug reactions, and in particular, the drug-drug interactions (DDIs). A drug-drug interaction occurs when two or more drugs are taken at the same time and one of them alters the action or the effect of the others [1]. Doctors have at their disposal several databases and abundant pharmacovigilance literature that allow them to detect and prevent DDIs [2]. Pharmacology is one of the areas of biomedical research with a growing number of publications (300,000 articles per year) [3]
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