@inproceedings{Wartena2022,
  author    = {Christian Wartena},
  title     = {On the Geometry of Concreteness},
  series = {Proceedings of the 7th Workshop on Representation Learning for NLP},
  doi       = {10.25968/opus-2299},
  url       = {http://nbn-resolving.de/urn:nbn:de:bsz:960-opus4-22996},
  pages     = {204 -- 212},
  year      = {2022},
  abstract  = {In this paper we investigate how concreteness and abstractness are represented in word embedding spaces. We use data for English and German, and show that concreteness and abstractness can be determined independently and turn out to be completely opposite directions in the embedding space. Various methods can be used to determine the direction of concreteness, always resulting in roughly the same vector. Though concreteness is a central aspect of the meaning of words and can be detected clearly in embedding spaces, it seems not as easy to subtract or add concreteness to words to obtain other words or word senses like e.g. can be done with a semantic property like gender.},
  language  = {en}
}