Reading level assessment using support vector machines and statistical language models

  • Authors:
  • Sarah E. Schwarm;Mari Ostendorf

  • Affiliations:
  • University of Washington, Seattle, WA;University of Washington, Seattle, WA

  • Venue:
  • ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Reading proficiency is a fundamental component of language competency. However, finding topical texts at an appropriate reading level for foreign and second language learners is a challenge for teachers. This task can be addressed with natural language processing technology to assess reading level. Existing measures of reading level are not well suited to this task, but previous work and our own pilot experiments have shown the benefit of using statistical language models. In this paper, we also use support vector machines to combine features from traditional reading level measures, statistical language models, and other language processing tools to produce a better method of assessing reading level.