@InProceedings{hayashi:2016:COLING,
  author    = {Hayashi, Yoshihiko},
  title     = {Predicting the Evocation Relation between Lexicalized Concepts},
  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     = {1657--1668},
  abstract  = {Evocation is a directed yet weighted semantic relationship between lexicalized
	concepts. Although evocation relations are considered potentially useful in
	several semantic NLP tasks, the prediction of the evocation relation between an
	arbitrary pair of concepts remains difficult, since evocation relationships
	cover a broader range of semantic relations rooted in human perception and
	experience. This paper presents a supervised learning approach to predict the
	strength (by regression) and to determine the directionality (by
	classification) of the evocation relation that might hold between a pair of
	lexicalized concepts. Empirical results that were obtained by investigating
	useful features are shown, indicating that a combination of the proposed
	features largely outperformed individual baselines, and also suggesting that
	semantic relational vectors computed from existing semantic vectors for
	lexicalized concepts were indeed effective for both the prediction of strength
	and the determination of directionality.},
  url       = {http://aclweb.org/anthology/C16-1156}
}