@InProceedings{koper-schulteimwalde:2017:SENSE2017,
  author    = {K\"{o}per, Maximilian  and  Schulte im Walde, Sabine},
  title     = {Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses},
  booktitle = {Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {24--30},
  abstract  = {Abstract words refer to things that can not be seen, heard, felt, smelled, or
	tasted as opposed to concrete words. Among other applications, the degree of
	abstractness has been shown to be a useful information for metaphor detection.
	Our
	contribution to this topic are as follows: i) we compare supervised techniques
	to
	learn and extend abstractness ratings for huge vocabularies ii) we learn and
	investigate norms for larger units by propagating abstractness to verb-noun
	pairs which lead to better metaphor detection iii) we overcome the limitation
	of learning a single rating per word and show that multi-sense abstractness
	ratings are potentially useful for metaphor detection. Finally, with this paper
	we publish automatically created abstractness norms for 3million English words
	and multi-words as well as automatically created sense specific abstractness
	ratings},
  url       = {http://www.aclweb.org/anthology/W17-1903}
}