Drug-Drug Interaction Extraction Based on Bidirectional Gated Recurrent Unit networks and Capsule Networks: OAJBS Publishers

Drug-Drug Interaction Extraction Based on Bidirectional Gated Recurrent Unit networks and Capsule Networks by Wenzhun Huang* in Open Access Journal of Biomedical Science (OAJBS)


Drug-drug interactions (DDIs) often result in unexpected side effects even death. DDI extraction (DDIE) is an important topic in the field of biomedical relationship extraction and has attracted great attention in natural language processing. The existing methods mainly emphasize the influence of entity, location and other information on DDIE, the maximum pooling of convolutional neural network (CNN) and Recurrent Neural Network results in the loss of feature information, and their extraction performance in long and complex sentences is still unsatisfactory. In this paper, a Gated Recurrent Unit and Capsule Networks (GRUTC) model is constructed for DDIE. Firstly, the original sentence sequence and the shortest dependency path (SDP) sequence are computed from the corpus and are input into two independent bidirectional Gated Recurrent Unit networks (Bi-GRU) to learn its semantic information and context information. Secondly, the extracted feature vectors are concatenated and input to the capsule network. Aiming at the problem of information loss caused by pooling strategy of CNN, the dynamic routing mechanism is employed to dynamically determine the amount of information transferred from low-level capsules to high-level capsules, so as to make full use of the high-level characteristic information and improve the DDIE effect. The experimental results on DDIExtraction2013 corpus show that the proposed method can effectively improve the performance without using any artificial features, and the F1-score is 73.7%, reaching the current advanced level.

https://biomedscis.com/fulltext/drug-drug-interaction-extraction-based-on-bidirectional-gated-recurrent-unit-networks-and-capsule-networks.ID.000247.php

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