Internationale Zeitschrift für Arzneimittelentwicklung und -forschung

• Tagebuch-Startseite
• Archiv
Autoren
• Autorenrichtlinien
• Manuskript einreichen
Im Detail
• Ziel und Umfang
• Publikationsethik
• Peer-Review-Prozess
• Urheberrechte ©
• Kontaktiere uns

Abstrakt

Extraction of Drug-Drug Interactions Using Convolutional Neural Networks

Puneet Souda*

Drug-drug interaction (DDI) extraction has long been a popular relation extraction task in natural language processing (NLP). Modern support vector machines (SVM) with a high number of manually set features are the foundation of most DDI extraction methods. Convolutional neural networks (CNN), a reliable machine learning technique that nearly never requires manually generated features, have recently shown significant promise for a variety of NLP tasks. CNN should be used for DDI extraction, which has never been looked at. A CNN-based technique for DDI extraction was put forth. CNN is a good option for DDI extraction, as shown by experiments done on the 2013 DDI Extraction challenge corpus. The CNN-based DDI extraction approach outperforms the currently highest performing method by 69.75%, achieving a score of 69.75%.

Drug-drug interaction (DDI); Convolutional neural networks (CNN); Support vector machines (SVM); Extraction