@InProceedings{forbes-choi:2017:Long,
  author    = {Forbes, Maxwell  and  Choi, Yejin},
  title     = {Verb Physics: Relative Physical Knowledge of Actions and Objects},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {266--276},
  abstract  = {Learning commonsense knowledge from natural language text is nontrivial due to
	reporting bias: people rarely state the obvious, e.g., “My house is bigger
	than me.” However, while rarely stated explicitly, this trivial everyday
	knowledge does influence the way people talk about the world, which provides
	indirect clues to reason about the world. For example, a statement like,
	“Tyler entered his house” implies that his house is bigger than Tyler.
	In this paper, we present an approach to infer relative physical knowledge of
	actions and objects along five dimensions (e.g., size, weight, and strength)
	from unstructured natural language text. We frame knowledge acquisition as
	joint inference over two closely related problems: learning (1) relative
	physical knowledge of object pairs and (2) physical implications of actions
	when applied to those object pairs. Empirical results demonstrate that it is
	possible to extract knowledge of actions and objects from language and that
	joint inference over different types of knowledge improves performance.},
  url       = {http://aclweb.org/anthology/P17-1025}
}