@InProceedings{madhyastha-carreras-quattoni:2017:IWPT,
  author    = {Madhyastha, Pranava Swaroop  and  Carreras, Xavier  and  Quattoni, Ariadna},
  title     = {Prepositional Phrase Attachment over Word Embedding Products},
  booktitle = {Proceedings of the 15th International Conference on Parsing Technologies},
  month     = {September},
  year      = {2017},
  address   = {Pisa, Italy},
  publisher = {Association for Computational Linguistics},
  pages     = {32--43},
  abstract  = {We present a low-rank multi-linear model for the task of solving prepositional
	phrase attachment ambiguity (PP task). Our model exploits tensor products of
	word embeddings, capturing all possible conjunctions of latent embeddings. Our
	results on a wide range of datasets and task settings show that tensor products
	are the best compositional operation and that a relatively simple multi-linear
	model that uses only word embeddings of lexical features can outperform more
	complex non-linear architectures that exploit the same information. Our
	proposed model gives the current best reported performance on an out-of-domain
	evaluation and performs competively on out-of-domain dependency parsing
	datasets.},
  url       = {http://www.aclweb.org/anthology/W17-6305}
}