Nursing-care text evaluation using word vector representations realized by word2vec

In this paper, we discuss a classification method of nursing-care texts using the word2vec [1]. The word2vec is a tool which provides the continuous bag-of-words and skip-gram implementations for realizing word vectors. We have tackled to classify nursing-care texts, which are freestyle Japanese texts, for improving nursing quality in several years. Several machine learning methods have been used for classifying such texts. To train a machine learning method, we used a word list which contains words appeared in the training data. Since the word list is a mere list, the relation among words is not considered. Also the length of the list depends on the number of words. Word vector representation proposed in [2]–[4] realized word representations in arbitrary dimensional space. We use the word2vec as a alternative word list in this paper. And we propose a new feature vector definition which is based on dependency structures in a text. From experimental results, we compare the proposed definition with our previous works.