Testing the Relationship between Word Length, Frequency, and Predictability Based on the German Reference Corpus

Autor: Alexander Koplenig, Marc Kupietz, Sascha Wolfer
Rok vydání: 2021
Předmět:
Zdroj: Cognitive scienceReferences. 46(6)
ISSN: 1551-6709
Popis: In a recent article, Meylan and Griffiths (MeylanGriffiths, 2021, henceforth, MG) focus their attention on the significant methodological challenges that can arise when using large-scale linguistic corpora. To this end, MG revisit a well-known result of Piantadosi, Tily, and Gibson (2011, henceforth, PTG) who argue that average information content is a better predictor of word length than word frequency. We applaud MG who conducted a very important study that should be read by any researcher interested in working with large-scale corpora. The fact that MG mostly failed to find clear evidence in favor of PTG's main finding motivated us to test PTG's idea on a subset of the largest archive of German language texts designed for linguistic research, the German Reference Corpus consisting of ∼43 billion words. We only find very little support for the primary data point reported by PTG.
Databáze: OpenAIRE