Konferensartikel

An Automatic Error Tagger for German

Inga Kempfert
Natural Lanuguage Systems Group, Department of Informatics, Universität Hamburg, Germany

Christine Köhn
Natural Lanuguage Systems Group, Department of Informatics, Universität Hamburg, Germany

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Ingår i: Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018) at SLTC, Stockholm, 7th November 2018

Linköping Electronic Conference Proceedings 152:4, s. 32-40

NEALT Proceedings Series 36:4, s. 32-40

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Publicerad: 2018-11-02

ISBN: 978-91-7685-173-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Automatically classifying errors by language learners facilitates corpus analysis and tool development. We present a tag set and a rule-based classifier for automatically assigning error tags to edits in learner texts. In our manual evaluation, the classifier assigns the best or close to best fitting tag in 92% of the cases.

Nyckelord

automatic error tagging, error classes, learner corpora, target hypotheses

Referenser

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