TY - JOUR
T1 - Predicting Text Comprehension, Processing, and Familiarity in Adult Readers
T2 - New Approaches to Readability Formulas
AU - Crossley, Scott A.
AU - Skalicky, Stephen
AU - Dascalu, Mihai
AU - McNamara, Danielle
AU - Kyle, Kristopher
N1 - Publisher Copyright:
© 2017 Taylor & Francis Group, LLC.
PY - 2017/7/4
Y1 - 2017/7/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85015611560&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015611560&partnerID=8YFLogxK
U2 - 10.1080/0163853X.2017.1296264
DO - 10.1080/0163853X.2017.1296264
M3 - Article
AN - SCOPUS:85015611560
SN - 0163-853X
VL - 54
SP - 340
EP - 359
JO - Discourse Processes
JF - Discourse Processes
IS - 5-6
ER -