Anzeige
Erschienen in:
2018 | OriginalPaper | Buchkapitel
The Automatic Generation of Nonwords for Lexical Recognition Tests
verfasst von : Osama Hamed, Torsten Zesch
Erschienen in: Human Language Technology. Challenges for Computer Science and Linguistics
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Lexical recognition tests are frequently used to assess vocabulary knowledge. In such tests, learners need to differentiate between words and artificial nonwords that look much like real words. Our ultimate goal is to create high quality lexical recognition tests automatically which enables repetitive automated testing for different languages. This task involves both simple (words selection) and complex (nonwords generation) subtasks. Our main goal here is to automatically generate word-like nonwords. 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.