@InProceedings{santus-EtAl:2017:EMNLP2017,
  author    = {Santus, Enrico  and  Chersoni, Emmanuele  and  Lenci, Alessandro  and  Blache, Philippe},
  title     = {Measuring Thematic Fit with Distributional Feature Overlap},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {648--658},
  abstract  = {In this paper, we introduce a new distributional method for modeling
	predicate-argument thematic fit judgments. 
	We use a syntax-based DSM to build a prototypical representation of
	verb-specific roles: for every verb, we extract the most salient second order
	contexts for each of its roles (i.e. the most salient dimensions of typical
	role fillers), and then we compute thematic fit as a weighted overlap between
	the top features of candidate fillers and role prototypes.
	Our experiments show that our method consistently outperforms a baseline
	re-implementing a state-of-the-art system, and achieves better or comparable
	results to those reported in the literature for the other unsupervised systems.
	Moreover, it provides an explicit representation of the features characterizing
	verb-specific semantic roles.},
  url       = {https://www.aclweb.org/anthology/D17-1068}
}