Japanese Sentence Order Estimation using Supervised Machine Learning with Rich Linguistic Clues

Estimation of sentence order (sometimes referred to as sentence ordering) is one of the problems that arise in sentence generation and sentence correction. When generating a text that consists of multiple sentences, it is necessary to arrange the sentences in an appropriate order so that the text can be understood easily. In this study, we proposed a new method using supervised machine learning with rich linguistic clues for Japanese sentence order estimation. As one of rich linguistic clues we used concepts on old information and new information. In Japanese, we can detect phrases containing old/new information by using Japanese topicmarking postpositional particles. In the experiments of sentence order estimation, the accuracies of our proposed method (0.72 to 0.77) were higher than those of the probabilistic method based on an existing method (0.58 to 0.61). We examined features using experiments and clarified which feature was important for sentence order estimation. We found that the feature using concepts on old information and new information was the most important.