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  • Predicting the Spelling Difficulty of Words for Language Learners

Predicting the Spelling Difficulty of Words for Language Learners

In many language learning scenarios, it is important to anticipate spelling errors. We model the spelling difficulty of words with new features that capture phonetic phenomena and are based on psycholinguistic findings. To train our model, we extract more than 140,000 spelling errors from three learner corpora covering English, German and Italian essays. The evaluation shows that our model predicts spelling difficulty with an accuracy of over 80% and yields a stable quality across corpora and languages. In addition, we provide a thorough error analysis that takes the native language of the learners into account and provides insights into cross-lingual transfer effects.

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Conference:
NAACL HLT 2016, The Eleventh Workshop on Innovative Use of NLP for Building Educational Applications, June 16, 2016 San Diego, California, USA
Date Issued:
2016
URN:
urn:nbn:de:hbz:464-20211130-085539-1
DOI:
10.18653/v1/W16-0508
Language:
English
Type of Resource:
Text
Collection:
E-Publications
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
URL:
https://www.aclweb.org/anthology/W16-0508
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
Info on primary publication:
Beinborn, L., Zesch, T., Gurevych, I. (2016): Predicting the Spelling Difficulty of Words for Language Learners. In: Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 73-83. Association for Computational Linguistics https://doi.org/10.18653/v1/W16-0508

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