@InProceedings{gharbieh-bhavsar-cook:2017:starSEM,
  author    = {Gharbieh, Waseem  and  Bhavsar, Virendrakumar  and  Cook, Paul},
  title     = {Deep Learning Models For Multiword Expression Identification},
  booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {54--64},
  abstract  = {Multiword expressions (MWEs) are lexical items that can be decomposed
	into multiple component words, but have properties that are
	unpredictable with respect to their component words. In this paper we
	propose the first deep learning models for token-level identification
	of MWEs. Specifically, we consider a layered feedforward network, a
	recurrent neural network, and convolutional neural networks. In
	experimental results we show that convolutional neural networks are
	able to outperform the previous state-of-the-art for MWE
	identification, with a convolutional neural network with three hidden
	layers giving the best performance.},
  url       = {http://www.aclweb.org/anthology/S17-1006}
}