Natural Language Processing in Artificial Neural Networks: Sentence analysis in medical papers

Abstract: Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field of Natural Language Processing (NLP) during the last years. In this project, CNNs are used on top of the Word2Vec word representation for a sentence classification task on medical research articles. Both individual networks for each category as well as a combined classification network are optimized and achieve average AUC scores of 0.89 and 0.82 respectively. A comparison with the results of a collaborating group using Support Vector Machines (SVMs) shows that simple CNNs can now compete with SVMs in this formerly SVM dominated area. In an extension, Recurrent Neural Networks (RNNs) are also trained on the same task and shown to be unfavorable compared to CNNs because of their lack of stability.