@InProceedings{fraser-EtAl:2017:EMNLP2017,
  author    = {Fraser, Kathleen C.  and  Lundholm Fors, Kristina  and  Kokkinakis, Dimitrios  and  Nordlund, Arto},
  title     = {An analysis of eye-movements during reading for the detection of mild cognitive impairment},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1016--1026},
  abstract  = {We present a machine learning analysis of eye-tracking data for the detection
	of mild cognitive impairment, a decline in cognitive abilities that is
	associated with an increased risk of developing dementia. We compare two
	experimental configurations (reading aloud versus reading silently), as well as
	two methods of combining information from the two trials (concatenation and
	merging). Additionally, we annotate the words being read with information about
	their frequency and syntactic category, and use these annotations to generate
	new features. Ultimately, we are able to distinguish between participants with
	and without cognitive impairment with up to 86% accuracy.},
  url       = {https://www.aclweb.org/anthology/D17-1107}
}