Generating Nonwords for Vocabulary Proficiency Testing

Lexical recognition tests are frequently used for measuring language proficiency. In such tests, learners need to differentiate between words and artificial nonwords that look much like real words. Our goal is to automatically generate word-like nonwords which enables repeated automated testing. We compare different ranking strategy and find that our best strategy (a specialized higher-order character-based language model) creates word-like nonwords. We evaluate our nonwords in a user study and find that our automatically generated test yields scores that are highly correlated with a well-established lexical recognition test which was manually created.

Vorschau

Einordnung

Konferenz:
7th Language and Technology Conference, LTC 2015, Poznań, Poland, November 27-29, 2015
Datum der Veröffentlichung:
2015
URN:
urn:nbn:de:hbz:464-20211022-103534-2
DOI:
10.17185/duepublico/72102
Sprache:
Englisch
Ressourcentyp:
Text
Kollektion:
E-Publikationen
Sachgruppen der Deutschen Nationalbibliographie:
004 Informatik
Einrichtung:
Fakultät für Ingenieurwissenschaften, Informatik und Angewandte Kognitionswissenschaft, Informatik, Sprachtechnologie
Informationen zur Erstveröffentlichung:
Hamed, Osama/Zesch, Torsten (2015): Generating Nonwords for Vocabulary Proficiency Testing. In: Proceedings of the 7th Language and Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics. Poznań, Poland, 2015, pp. 473–477. http://ltc.amu.edu.pl/a2015/book/papers/APP-1.pdf

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Dieses Werk kann unter einer
CC BY-NC-SA 4.0 LogoCreative Commons Namensnennung - Nicht-kommerziell - Weitergabe unter gleichen Bedingungen 4.0 Lizenz (CC BY-NC-SA 4.0)
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