Automatic orthographic error tagging and classification for German texts. (November 2018) - Berkling, Kay; Lavalley, Rémi
Highlights: An automatic spelling error tagger and classifier for German texts is proposed. Each of the categories is evaluated by comparing the system to a manual tagging on 2000 words. The tool is applied to a longitudinal study consisting of weekly children's writings. Abstract: This paper evaluates an automatic spelling error tagger and classifier for German texts. After explaining the existing error tags in detail, the accuracy of the tool is validated against a publicly available database containing around 1700 written texts ranging from first grade to eighth grade. The tool is then applied to a longitudinal study consisting of weekly children's texts from second and third grades. It can be shown which error categories contribute most significantly to children's error profiles. Additionally, it can be shown whether or not children make progress on improving in the categories under study.