@InProceedings{krnap-onder-yuret:2017:K17-3,
  author    = {Kırnap, \"{O}mer  and  \"{O}nder, Berkay Furkan  and  Yuret, Deniz},
  title     = {Parsing with Context Embeddings},
  booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {80--87},
  abstract  = {We introduce context embeddings, dense vectors derived from a language model
	that represent the left/right context of a word instance, and demonstrate that
	context embeddings significantly improve the accuracy of our transition based
	parser. Our model consists of a bidirectional LSTM (BiLSTM) based language
	model that is pre-trained to predict words in plain text, and a multi-layer
	perceptron (MLP) decision model that uses features from the language model to
	predict the correct actions for an ArcHybrid transition based parser. We
	participated in the CoNLL 2017 UD Shared Task as the ``Ko\c{c} University'' team
	and our system was ranked 7th out of 33 systems that parsed 81 treebanks in 49
	languages.},
  url       = {http://www.aclweb.org/anthology/K17-3008}
}