Predicting the Spelling Difficulty of Words for Language Learners
| Autor: | Iryna Gurevych, Lisa Beinborn, Torsten Zesch |
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| Přispěvatelé: | NAACL HLT 2016, The Eleventh Workshop on Innovative Use of NLP for Building Educational Applications, June 16, 2016 San Diego, California, USA, Language |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
First language media_common.quotation_subject 02 engineering and technology computer.software_genre 050105 experimental psychology German Error analysis 0202 electrical engineering electronic engineering information engineering Fakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Sprachtechnologie 0501 psychology and cognitive sciences Quality (business) media_common business.industry 05 social sciences Language acquisition Linguistics language.human_language Spelling Spelling difficulty Informatik Learner corpora language 020201 artificial intelligence & image processing Artificial intelligence ddc:004 business computer Natural language processing |
| Zdroj: | BEA@NAACL-HLT 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 . Association for Computational Linguistics, pp. 73-83 . < https://www.aclweb.org/anthology/W16-0508 > Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, 73-83 STARTPAGE=73;ENDPAGE=83;TITLE=Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications |
| DOI: | 10.18653/v1/w16-0508 |
| Popis: | 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 can predict 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. |
| Databáze: | OpenAIRE |
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