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

摘要

In this paper, we discuss a classification method of nursing-care texts using the word2vec. 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 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.