Paper: Cognitively Motivated Features for Readability Assessment

ACL ID E09-1027
Title Cognitively Motivated Features for Readability Assessment
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors
  • Lijun Feng (City University of New York-Graduate Center, New York NY)
  • Noémie Elhadad (Columbia University, New York NY)
  • Matt Huenerfauth (City University of New York-Queens College, Flushing NY; City University of New York-Graduate Center, New York NY)

We investigate linguistic features that correlate with the readability of texts for adults with in- tellectual disabilities (ID). Based on a corpus of texts (including some experimentally meas- ured for comprehension by adults with ID), we analyze the significance of novel discourse- level features related to the cognitive factors underlying our users’ literacy challenges. We develop and evaluate a tool for automatically rating the readability of texts for these users. Our experiments show that our discourse- level, cognitively-motivated features improve automatic readability assessment.