Abstract
Picture books have a significant influence on children’s language development. However, the sentences in picture books are difficult to analyze automatically. Therefore, to improve the accuracy of the morphological analysis of such sentences, we propose an automatic method to transform existing resources into applicable training data for picture books. In this paper, we first compare picture books with common corpora and then analyze the reasons for the difficulty in morphological analysis. Based on this analysis, we propose a transforming method for existing resources and show its effectiveness using the learning function of an existing morphological analyzer. Second, we perform further experiments using annotated data of picture books themselves. Then we reveal that our proposed method provides us with the same effect, with around 11,000 lines, that is 90,000 morphological annotations of picture books. In addition, we demonstrate an effective annotation strategy by investigating the learning curves and change in error types. In a discussion, we analyze the results focused on a picture book’s target ages and difficult to learn words and then further refine our proposed method. Finally, we also briefly consider the applicability of our method to other domains.
