Cognitively Motivated Features for Readability Assessment

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 229–237, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Cognitively Motivated Features for Readability Assessment Lijun Feng Noémie Elhadad Matt Huenerfauth The City University of New York, Columbia University The City University of New York, Graduate Center New York, NY, USA Queens College & Graduate Center New York, NY, USA noemie@dbmi.columbia.edu New York, NY, USA lijun7.feng@gmail.com matt@cs.qc.cuny.edu Abstract 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. 1 Introduction Assessing the degree of readability of a text has been a field of research as early as the 1920's. Dale and Chall define readability as “the sum total (including all the interactions) of all those elements within a given piece of printed material that affect the success a group of readers have with it. The success is the extent to which they understand it, read it at optimal speed, and find it interesting” (Dale and Chall, 1949). It has long been acknowledged that readability is a function of text characteristics, but also of the readers themselves. The literacy skills of the readers, their motivations, background knowledge, and other internal characteristics play an important role in determining whether a text is readable for a particular group of people. In our work, we investigate how to assess the readability of a text for people with intellectual disabilities (ID). Previous work in automatic readability as- sessment has focused on generic features of a text at the lexical and syntactic level. While such features are essential, we argue that audience- specific features that model the cognitive charac- teristics of a user group can improve the accura- cy of a readability assessment tool. The contri- butions of this paper are: (1) we present a corpus of texts with readability judgments from adults with ID; (2) we propose a set of cognitively- motivated features which operate at the discourse level; (3) we evaluate the utility of these features in predicting readability for adults with ID. Our framework is to create tools that benefit people with intellectual disabilities (ID), specifi- cally those classified in the “mild level” of men- tal retardation, IQ scores 55-70. About 3% of the U.S. population has intelligence test scores of 70 or lower (U.S. Census Bureau, 2000). People with ID face challenges in reading literacy. They are better at decoding words (sounding them out) than at comprehending their meaning (Drew & Hardman, 2004), and most read below their men- tal age-level (Katims, 2000). Our research ad- dresses two literacy impairments that …

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