Abstract

This article presents a novel approach for readability assessment through sorting. A comparator that judges the relative readability between two texts is generated through machine learning, and a given set of texts is sorted by this comparator. Our proposal is advantageous because it solves the problem of a lack of training data, because the construction of the comparator only requires training data annotated with two reading levels. The proposed method is compared with regression methods and a state-of-the art classification method. Moreover, we present our application, called Terrace, which retrieves texts with readability similar to that of a given input text.

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Author notes

* University of Tokyo Cross Field, 13F Akihabara Daibiru, 1-18-13 Kanda Chiyoda-ku, Tokyo, Japan. E-mail: kumiko@kumish.net.