@InProceedings{cattle-ma:2017:EMNLP2017,
  author    = {Cattle, Andrew  and  Ma, Xiaojuan},
  title     = {Predicting Word Association Strengths},
  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     = {1283--1288},
  abstract  = {This paper looks at the task of predicting word association strengths across
	three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of
	Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh
	Associative Thesaurus (Kiss et al., 1973). We achieve results of r=0.357 and
	p=0.379, r=0.344 and p=0.300, and r=0.292 and p=0.363, respectively. We find
	Word2Vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014) cosine
	similarities, as well as vector offsets, to be the highest performing features.
	Furthermore, we examine the usefulness of Gaussian embeddings (Vilnis and
	McCallum, 2014) for predicting word association strength, the first work to do
	so.},
  url       = {https://www.aclweb.org/anthology/D17-1132}
}