Revisiting Readability: A Unified Framework for Predicting Text Quality

We combine lexical, syntactic, and discourse features to produce a highly predictive model of human readers' judgments of text readability. This is the first study to take into account such a variety of linguistic factors and the first to empirically demonstrate that discourse relations are strongly associated with the perceived quality of text. We show that various surface metrics generally expected to be related to readability are not very good predictors of readability judgments in our Wall Street Journal corpus. We also establish that readability predictors behave differently depending on the task: predicting text readability or ranking the readability. Our experiments indicate that discourse relations are the one class of features that exhibits robustness across these two tasks.

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Emily Pitler, Ani Nenkova
• Keywords: Discourse Relations, EMNLP 2008, Natural Language Processing, Readability Judgments, Text Readability