@InProceedings{singh-EtAl:2016:CL4LC,
  author    = {Singh, Abhinav Deep  and  Mehta, Poojan  and  Husain, Samar  and  Rajakrishnan, Rajkumar},
  title     = {Quantifying sentence complexity based on eye-tracking measures},
  booktitle = {Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {202--212},
  abstract  = {Eye-tracking reading times have been attested to reflect cognitive processes
	underlying sentence comprehension. However, the use of reading times in NLP
	applications is an underexplored area of research. In this initial work we
	build an automatic system to assess sentence complexity using automatically
	predicted eye-tracking reading time measures and demonstrate the efficacy of
	these reading times for a well known NLP task, namely, readability assessment.
	We use a machine learning model and a set of features known to be significant
	predictors of reading times in order to learn per-word reading times from a
	corpus of English text having reading times of human readers. Subsequently, we
	use the model to predict reading times for novel text in the context of the
	aforementioned task. A model based only on reading times gave competitive
	results compared to the systems that use extensive syntactic features to
	compute linguistic complexity. Our work, to the best of our knowledge, is the
	first study to show that automatically predicted reading times can successfully
	model the difficulty of a text and can be deployed in practical text processing
	applications.},
  url       = {http://aclweb.org/anthology/W16-4123}
}