@InProceedings{raganato-camachocollados-navigli:2017:EACLlong,
  author    = {Raganato, Alessandro  and  Camacho-Collados, Jose  and  Navigli, Roberto},
  title     = {Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {99--110},
  abstract  = {Word Sense Disambiguation is a long-standing task in Natural Language
	Processing, lying at the core of human language understanding. However, the
	evaluation of automatic systems has been problematic, mainly due to the lack of
	a reliable evaluation framework. In this paper we develop a unified evaluation
	framework and analyze the performance of various Word Sense Disambiguation
	systems in a fair setup. The results show that supervised systems clearly
	outperform knowledge-based models. Among the supervised systems, a linear
	classifier trained on conventional local features still proves to be a hard
	baseline to beat. Nonetheless, recent approaches exploiting neural networks on
	unlabeled corpora achieve promising results, surpassing this hard baseline in
	most test sets.},
  url       = {http://www.aclweb.org/anthology/E17-1010}
}