Improved Convolutional Neural Network for Biomedical Text Classification

In recent years, the combination of biomedical field and computer field is booming. Obtaining useful information from a large amount of biomedical text information is a research topic of great significance. Convolutional neural network has a good ability to extract useful features, so it is widely used in the field of text classification. In this paper, a novel approach for biomedical text classification based on improved convolutional neural network is proposed to solve the problem that deep convolutional neural network has a large amount of computation and can not perceive the relationship between levels well. In this paper, we use the combination of deep separable convolution and void convolution to improve the convolutional neural network. At the same time, we use the attention mechanism to classify biomedical literature. In addition, focusing loss function is used to improve the imbalance of biomedical texts. Experimental results show that the classification model in this paper is effective for biomedical texts.