@InProceedings{kober-EtAl:2017:Short,
  author    = {Kober, Thomas  and  Weeds, Julie  and  Reffin, Jeremy  and  Weir, David},
  title     = {Improving Semantic Composition with Offset Inference},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {433--440},
  abstract  = {Count-based distributional semantic models suffer from sparsity due to
	unobserved but plausible co-occurrences in any text collection. This problem is
	amplified for models like Anchored Packed Trees (APTs), that take the
	grammatical type of a co-occurrence into account. We therefore introduce a
	novel form of distributional inference that exploits the rich type structure in
	APTs and infers missing data by the same mechanism that is used for semantic
	composition.},
  url       = {http://aclweb.org/anthology/P17-2069}
}