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 |
| Zugehörige Links: | |
| 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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