@InProceedings{dedeyne-perfors-navarro:2016:COLING,
  author    = {De Deyne, Simon  and  Perfors, Amy  and  Navarro, Daniel J},
  title     = {Predicting human similarity judgments with distributional models: The value of word associations.},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1861--1870},
  abstract  = {Most distributional lexico-semantic models derive their representations based
	on external language resources such as text corpora. In this study, we propose
	that internal language models, that are more closely aligned to the mental
	representations of words could provide important insights into cognitive
	science, including linguistics. Doing so allows us to reflect upon theoretical
	questions regarding the structure of the mental lexicon, and also puts into
	perspective a number of assumptions underlying recently proposed distributional
	text-based models.
	In particular, we focus on word-embedding models which have been proposed to
	learn aspects of word meaning in a manner similar to humans. These are
	contrasted with internal language models derived from a new extensive data set
	of word associations. 
	Using relatedness and similarity judgments we evaluate these models and find
	that the word-association-based internal language models consistently
	outperform current state-of-the art text-based external language models, often
	with a large margin. These results are not just a performance improvement; they
	also have implications for our understanding of how distributional knowledge is
	used by people.},
  url       = {http://aclweb.org/anthology/C16-1175}
}