@InProceedings{wartena-aga:2016:CogALex-V,
  author    = {Wartena, Christian  and  Aga, Rosa Tsegaye},
  title     = {CogALex-V Shared Task: HsH-Supervised -- Supervised similarity learning using entry wise product of context vectors},
  booktitle = {Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {114--118},
  abstract  = {The CogALex-V Shared Task provides two datasets  that consists of pairs of
	words along with a classification of their semantic relation. The dataset for
	the first task distinguishes only between related and unrelated, while the
	second data set distinguishes several types of semantic relations. A number of
	recent papers propose to construct a feature vector that represents a pair of
	words by applying a pairwise simple operation to all elements of the feature
	vector. Subsequently, the pairs can be classified by training any
	classification algorithm on these vectors. In the present paper we apply this
	method to the provided datasets. We see that the results are not better than
	from the given simple baseline. We conclude that the results of the
	investigated method are strongly depended on the type of data to which it is
	applied.},
  url       = {http://aclweb.org/anthology/W16-5316}
}