@InProceedings{attia-EtAl:2016:CogALex-V,
  author    = {Attia, Mohammed  and  Maharjan, Suraj  and  Samih, Younes  and  Kallmeyer, Laura  and  Solorio, Thamar},
  title     = {CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings},
  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     = {86--91},
  abstract  = {This paper describes our system submission to the CogALex-2016 Shared Task on
	Corpus-Based Identification of Semantic Relations. Our system won first place
	for Task-1 and second place for Task-2. The evaluation results of our system on
	the test set is 88.1% (79.0% for TRUE only) f-measure for Task-1 on detecting
	semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task-2 on
	identifying finer-grained semantic relations. In our experiments, we try word
	analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs)
	with word embeddings from publicly available word vectors. We found that linear
	regression performs better in the binary classification (Task-1), while CNNs
	have better performance in the multi-class semantic classification (Task-2).
	We assume that word analogy is more suited for deterministic answers rather
	than handling the ambiguity of one-to-many and many-to-many relationships. We
	also show that classifier performance could benefit from balancing the
	distribution of labels in the training data.},
  url       = {http://aclweb.org/anthology/W16-5311}
}