@InProceedings{howcroft-demberg:2017:EACLlong,
  author    = {Howcroft, David M.  and  Demberg, Vera},
  title     = {Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {958--968},
  abstract  = {While previous research on readability has typically focused on document-level
	measures, recent work in areas such as natural language generation has pointed
	out the need of sentence-level readability measures.  Much of psycholinguistics
	has focused for many years on processing measures that provide difficulty
	estimates on a word-by-word basis. However, these psycholinguistic measures
	have not yet been tested on sentence readability ranking tasks.  In this paper,
	we use four psycholinguistic measures: idea density, surprisal, integration
	cost, and embedding depth to test whether these features are predictive of
	readability levels. We find that psycholinguistic features significantly
	improve performance by up to 3 percentage points over a standard document-level
	readability metric baseline.},
  url       = {http://www.aclweb.org/anthology/E17-1090}
}