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Predicting the Spelling Difficulty of Words for Language Learners

Beinborn, Lisa ; Zesch, Torsten ; Gurevych, Iryna (2016)
Predicting the Spelling Difficulty of Words for Language Learners.
San Diego, CA, USA
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

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.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2016
Autor(en): Beinborn, Lisa ; Zesch, Torsten ; Gurevych, Iryna
Art des Eintrags: Bibliographie
Titel: Predicting the Spelling Difficulty of Words for Language Learners
Sprache: Englisch
Publikationsjahr: 2016
Buchtitel: Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications held in conjunction with NAACL 2016
Veranstaltungsort: San Diego, CA, USA
URL / URN: http://m-mitchell.com/NAACL-2016/BEA/pdf/BEA1108.pdf
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Kurzbeschreibung (Abstract):

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.

Freie Schlagworte: UKP_a_WALL;UKP_p_AutoExerGen;UKP_a_ENLP;UKP_reviewed
ID-Nummer: TUD-CS-2016-0077
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen
Hinterlegungsdatum: 31 Dez 2016 14:29
Letzte Änderung: 24 Jan 2020 12:03
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