@InProceedings{hartung-EtAl:2017:EACLlong,
  author    = {Hartung, Matthias  and  Kaupmann, Fabian  and  Jebbara, Soufian  and  Cimiano, Philipp},
  title     = {Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {54--64},
  abstract  = {Word embeddings have been shown to be highly effective in a variety of lexical
	semantic tasks. They tend to capture meaningful relational similarities between
	individual words, at the expense of lacking the capabilty of making the
	underlying semantic relation explicit. In this paper, we investigate the
	attribute relation that often holds between the constituents of adjective-noun
	phrases. We use CBOW word embeddings to represent word meaning and learn a
	compositionality function that combines the individual constituents into a
	phrase representation, thus capturing the compositional attribute meaning. The
	resulting embedding model, while being fully interpretable, outperforms
	count-based distributional vector space models that are tailored to attribute
	meaning in the two tasks of attribute selection and phrase similarity
	prediction. Moreover, as the model captures a generalized layer of attribute
	meaning, it bears the potential to be used for predictions over various
	attribute inventories without re-training.},
  url       = {http://www.aclweb.org/anthology/E17-1006}
}