@InProceedings{claveau-kijak:2016:COLING,
  author    = {Claveau, Vincent  and  Kijak, Ewa},
  title     = {Direct vs. indirect evaluation of distributional thesauri},
  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     = {1837--1848},
  abstract  = {With the success of word embedding methods in various Natural Language
	Processing tasks,
	all the field of distributional semantics has experienced a renewed interest.
	Beside the famous
	word2vec, recent studies have presented efficient techniques to build
	distributional thesaurus; in
	particular, Claveau et al. (2014) have already shown that Information Retrieval
	(IR) tools and
	concepts can be successfully used to build a thesaurus. In this paper, we
	address the problem
	of the evaluation of such thesauri or embedding models and compare their
	results. Through
	several experiments and by evaluating directly the results with reference
	lexicons, we show that
	the recent IR-based distributional models outperform state-of-the-art systems
	such as word2vec.
	Following the work of Claveau and Kijak (2016), we use IR as an applicative
	framework to
	indirectly evaluate the generated thesaurus. Here again, this task-based
	evaluation validates the
	IR approach used to build the thesaurus. Moreover, it allows us to compare
	these results with
	those from the direct evaluation framework used in the literature. The observed
	differences bring
	these evaluation habits into question.},
  url       = {http://aclweb.org/anthology/C16-1173}
}