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Title: Using text-based indices, one can predict how lexically rich short texts in French, German, and Portuguese will be perceived.
Abstract: We looked at how well readers’ judgments of the lexical richness of short texts may be predicted by automated computations of the lexical properties of the texts. This was accomplished by examining the texts’ lexical features. From the writings of 3,060 kids between the ages of 8 and 10 written in French, German, and Portuguese, more than 150 indices were created. They were evaluated for their lexical richness by three and eighteen non-educated raters. The texts were from 9 and 284 words long. We found that the ratings of shorter texts could be predicted with a comparable degree of accuracy to those of longer texts, and that the ratings could be primarily based on these indices. On the other hand, models with fewer predictors based on a 6-dimensional framework of lexical richness perceptions or even just a simple predictor, Guiraud’s index, outperformed models with more predictors by a small margin. For French and German, opaque models with a large number of variables were the most effective predictors.
Keywords: human ratings, lexical diversity, lexical sophistication, lexical richness, modelling predictive
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Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: ELT – English Language Teaching
Writer Experience: 20+ Years
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