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Predicting the spelling difficulty of words for language learners
Beinborn, Lisa; Zesch, Torsten; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistic (Hrsg.): Proceedings of the 11th workshop on innovative use of NLP for building educational applications held in conjunction with NAACL 2016 | Stroudsburg; PA: Association for Computational Linguistics | 2016
36973 Endnote
Autor*innen:
Beinborn, Lisa; Zesch, Torsten; Gurevych, Iryna
Titel:
Predicting the spelling difficulty of words for language learners
Aus:
Association for Computational Linguistic (Hrsg.): Proceedings of the 11th workshop on innovative use of NLP for building educational applications held in conjunction with NAACL 2016, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 73-83
URL:
https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/wall/BEA2016_SpellingDifficulty.pdf
Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Computerlinguistik; Deutsch; Englisch; Fehler; Fremdsprache; Italienisch; Modell; Muttersprache; Phonetik; Psycholinguistik; Rechtschreibung
Abstract (english):
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. (DIPF/Orig.)
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