@InProceedings{delhoneux-EtAl:2017:K17-3,
  author    = {de Lhoneux, Miryam  and  Shao, Yan  and  Basirat, Ali  and  Kiperwasser, Eliyahu  and  Stymne, Sara  and  Goldberg, Yoav  and  Nivre, Joakim},
  title     = {From Raw Text to Universal Dependencies - Look, No Tags!},
  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     = {207--217},
  abstract  = {We present the Uppsala submission to the CoNLL 2017 shared task on parsing from
	raw text to universal dependencies. Our system is a simple pipeline consisting
	of two components. The first performs joint word and sentence segmentation on
	raw text; the second predicts dependency trees from raw words. The parser
	bypasses the need for part-of-speech tagging, but uses word embeddings based on
	universal tag distributions. We achieved a macro-averaged LAS F1 of 65.11 in
	the official test run, which improved to 70.49 after bug fixes. We obtained the
	2nd best result for sentence segmentation with a score of 89.03.},
  url       = {http://www.aclweb.org/anthology/K17-3022}
}