@InProceedings{garimella-banea-mihalcea:2017:EMNLP2017,
  author    = {Garimella, Aparna  and  Banea, Carmen  and  Mihalcea, Rada},
  title     = {Demographic-aware word associations},
  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     = {2285--2295},
  abstract  = {Variations of word associations across different groups of people can provide
	insights into people’s psychologies and their world views. To capture these
	variations, we introduce the task of demographic-aware word associations. We
	build a new gold standard dataset consisting of word association responses for
	approximately 300 stimulus words, collected from more than 800 respondents of
	different gender (male/female) and from different locations (India/United
	States), and show that there are significant variations in the word
	associations made by these groups. We also introduce a new demographic-aware
	word association model based on a neural net skip-gram architecture, and show
	how computational methods for measuring word associations that specifically
	account for writer demographics can outperform generic methods that are
	agnostic to such information.},
  url       = {https://www.aclweb.org/anthology/D17-1242}
}