@InProceedings{more-tsarfaty:2017:K17-3,
  author    = {More, Amir  and  Tsarfaty, Reut},
  title     = {Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task},
  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     = {253--264},
  abstract  = {We present the Open University's submission to the CoNLL 2017 Shared Task on
	multilingual parsing from raw text to Universal Dependencies.
	The core of our system is a joint morphological disambiguator and syntactic
	parser which accepts morphologically analyzed surface tokens as input and
	returns morphologically disambiguated dependency trees as output.
	Our parser requires a lattice as input, so we generate morphological analyses
	of surface tokens using a data-driven morphological analyzer that derives its
	lexicon from the UD training corpora, and we rely on UDPipe for sentence
	segmentation and surface-level tokenization. We report our official
	macro-average LAS is 56.56. Although our model is not as performant as many
	others, it does not make use of neural networks, therefore we do not rely on
	word embeddings or any other data source other than the corpora themselves.
	In addition, we show the utility of a lexicon-backed morphological analyzer for
	the MRL Modern Hebrew. We use our results on Modern Hebrew to argue that the UD
	community should define a UD-compatible standard for access to lexical
	resources, which we argue is crucial for MRLs and low resource languages in
	particular.},
  url       = {http://www.aclweb.org/anthology/K17-3027}
}