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Order Estimation of Japanese Paragraphs by Supervised Machine Learning and Various Textual Features

Abstract In this paper, we propose a method to estimate the order of paragraphs by supervised machine learning. We use a support vector machine (SVM) for supervised machine learning. The estimation of paragraph order is useful for sentence generation and sentence correction. The proposed method obtained a high accuracy (0.84) in the order estimation experiments of the first two paragraphs of an article. In addition, it obtained a higher accuracy than the baseline method in the experiments using two paragraphs of an article. We performed feature analysis and we found that adnominals, conjunctions, and dates were effective for the order estimation of the first two paragraphs, and the ratio of new words and the similarity between the preceding paragraphs and an estimated paragraph were effective for the order estimation of all pairs of paragraphs.