Automatic orthographic error tagging and classification for German texts. (November 2018)

• Record Type: Journal Article
• Authors: Berkling, Kay; Lavalley, Rémi
• Is Part Of: Computer speech & language. Volume 52(2018)
• Page Start: 56
• Page End: 78
• Publication Date: 2018-11
• Subjects: Orthography -- Children's text -- Corpus -- Longitudinal study; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.11.002
• Languages: English
• ISSNs: 0885-2308

Abstract

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.