A Convolution Neural Network Based Nursing-Care Text Classification Model with a New Filter for Expressing Dependency Relations of Words

In this paper, a convolution neural network (CNN) based text classification method is proposed. CNNs show strong performance for computer vision and speech recognition applications. Recently, in some researches, CNNs have been applied to sentence classification applications. Currently, we have studied nursing-care text classification to improve nursing-care quality in Japan. In our former works, several types of feature definitions have been proposed and examined by some classification models like SVMs. In this paper, a single layer CNN is used for classifying nursing-care texts. Each nursing-care text is represented as a concatenated word vectors. Each word is represented as a fixed length word vector which is obtained by the word2vec. Then, nursing-care texts are classified using a two-dimensional CNN-based classification method. The proposed CNN has a new kind of filters which extracts dependency relation between words. From our experimental results, the proposed CNN-based method obtained better performance than our former works.