Since a large amount of medical treatments requires the assumption of multiple drugs, the discovery of how these interact with each other, potentially causing health problems to the patients, is the subject of a huge quantity of documents. In order to obtain this information from free text, several methods involving deep learning have been proposed over the years. In this paper we introduce a Recurrent Neural Network-based method combined with the Self-Interaction Attention Mechanism. Such a method is applied to the DDI2013-Extraction task, a popular challenge concerning the extraction and the classification of drug-drug interactions. Our focus is to show its effect over the tendency to predict the majority class and how it differs from the other types of attention mechanisms.
|Titolo:||The Impact of Self-Interaction Attention on the Extraction of Drug-Drug Interactions|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|