@InProceedings{yamane-EtAl:2016:COLING,
  author    = {Yamane, Josuke  and  Takatani, Tomoya  and  Yamada, Hitoshi  and  Miwa, Makoto  and  Sasaki, Yutaka},
  title     = {Distributional Hypernym Generation by Jointly Learning Clusters and Projections},
  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     = {1871--1879},
  abstract  = {We propose a novel word embedding-based hypernym generation model that jointly
	learns clusters of hyponym-hypernym relations, i.e., hypernymy, and projections
	from hyponym to hypernym embeddings. Most of the recent hypernym detection
	models focus on a hypernymy classification problem that determines whether a
	pair of words is in hypernymy or not. These models do not directly deal with a
	hypernym generation problem in that a model generates hypernyms for a given
	word. Differently from previous studies, our model jointly learns the clusters
	and projections with adjusting the number of clusters so that the number of
	clusters can be determined depending on the learned projections and vice versa.
	Our model also boosts the performance by incorporating inner product-based
	similarity measures and negative examples, i.e., sampled non-hypernyms, into
	our objectives in learning. We evaluated our joint learning models on the task
	of Japanese and English hypernym generation and showed a significant
	improvement over an existing pipeline model. Our model also compared favorably
	to existing distributed hypernym detection models on the English hypernym
	classification task.},
  url       = {http://aclweb.org/anthology/C16-1176}
}