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Conference Papers Year : 2018

Predicting Text Readability with Personal Pronouns

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Boyang Sun
  • Function : Author
Ming Yue
  • Function : Author
  • PersonId : 1046665

Abstract

While the classic Readability Formula exploits word and sentence length, we aim to test whether Personal Pronouns (PPs) can be used to predict text readability with similar accuracy or not. Out of this motivation, we first calculated readability score of randomly selected texts of nine genres from the British National Corpus (BNC). Then we used Multiple Linear Regression (MLR) to determine the degree to which readability could be explained by any of the 38 individual or combinational subsets of various PPs in their orthographical forms (including I, me, we, us, you, he, him, she, her (the Objective Case), it, they and them). Results show that (1) subsets of plural PPs can be more predicative than those of singular ones; (2) subsets of Objective forms can make better predictions than those of Subjective ones; (3) both the subsets of first- and third-person PPs show stronger predictive power than those of second-person PPs; (4) adding the article the to the subsets could only improve the prediction slightly. Reevaluation with resampled texts from BNC verify the practicality of using PPs as an alternative approach to predict text readability.
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Dates and versions

hal-02118839 , version 1 (03-05-2019)

Licence

Attribution - CC BY 4.0

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Boyang Sun, Ming Yue. Predicting Text Readability with Personal Pronouns. 2nd International Conference on Intelligence Science (ICIS), Nov 2018, Beijing, China. pp.255-264, ⟨10.1007/978-3-030-01313-4_27⟩. ⟨hal-02118839⟩
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