Developing Linguistic Constructs of Text Readability Using Natural Language Processing

Abstract

ABSTRACT Purpose Understanding what makes a text difficult to read is an important element of literacy instruction, materials development, and research into text readability. Readability formulas are often used to quantify text readability. However, readability formulas often focus on a relatively limited number of text features that inform the reading process and/or use single features to measure complex language constructs under the presumption that related features do not interact with one another. As a result, the formulas can be difficult to interpret by practitioners. Method A principal component analysis was used to combine text features from advanced natural language processing tools into theoretically-inspired language constructs related to text comprehension. Result Results indicate that text features aggregated into seven components related to informational text, syntactic constructions, noun phrase complexity, lexical ease, function word ease, narrativity, and lexical variety. These components predicted 44% of the variance in readability scores reported in a large readability corpus. Conclusion The results provide evidence that individual linguistic features related to text readability can be combined into reading components that are strong predictors of text comprehension. Practically, these components should be more easily interpretable allowing for more actionable feedback and use by practitioners.