Predicting Text Comprehension, Processing, and Familiarity in Adult Readers: New Approaches to Readability Formulas

Scott A. Crossley, Stephen Skalicky, Mihai Dascalu, Danielle McNamara, Kristopher Kyle

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Research has identified a number of linguistic features that influence the reading comprehension of young readers; yet, less is known about whether and how these findings extend to adult readers. This study examines text comprehension, processing, and familiarity judgment provided by adult readers using a number of different approaches (i.e., natural language processing, crowd-sourced ratings, and machine learning). The primary focus is on the identification of the linguistic features that predict adult text readability judgments, and how these features perform when compared to traditional text readability formulas such as the Flesch-Kincaid grade level formula. The results indicate the traditional readability formulas are less predictive than models of text comprehension, processing, and familiarity derived from advanced natural language processing tools.

Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalDiscourse Processes
DOIs
Publication statusAccepted/In press - Mar 19 2017

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ASJC Scopus subject areas

  • Language and Linguistics
  • Communication
  • Linguistics and Language

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