@InProceedings{roesiger-EtAl:2016:Computerm2016,
  author    = {Roesiger, Ina  and  Bettinger, Julia  and  Sch\"{a}fer, Johannes  and  Dorna, Michael  and  Heid, Ulrich},
  title     = {Acquisition of semantic relations between terms: how far can we get with standard NLP tools?},
  booktitle = {Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {41--51},
  abstract  = {The extraction of data exemplifying relations between terms can make use, at
	least to a large extent, of techniques that are similar to those used in
	standard hybrid term candidate extraction, namely basic corpus analysis tools
	(e.g. tagging, lemmatization, parsing), as well as morphological analysis of
	complex words (compounds and derived items). In this article, we discuss the
	use of such techniques for the extraction of raw material for a description of
	relations between terms, and we provide internal evaluation data for the
	devices developed.
	We claim that user-generated content is a rich source of term variation through
	paraphrasing and reformulation, and that these provide relational data at the
	same time as term variants. Germanic languages with their rich word formation
	morphology may be particularly good candidates for the approach advocated here.},
  url       = {http://aclweb.org/anthology/W16-4706}
}