@InProceedings{qian-liu:2017:K17-3,
  author    = {Qian, Xian  and  Liu, Yang},
  title     = {A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at 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     = {143--151},
  abstract  = {For this year’s multilingual dependency parsing shared task, we developed a
	pipeline system, which uses a variety of features for each of its components.
	Unlike the recent popular deep learning approaches that learn low dimensional
	dense features using non-linear classifier, our system uses structured linear
	classifiers to learn millions of sparse features. Specifically, we trained a
	linear classifier for sentence boundary prediction, linear chain conditional
	random fields (CRFs) for tokenization, part-of-speech tagging and morph
	analysis. A second order graph based parser learns the tree structure (without
	relations), and fa linear tree CRF then assigns relations to the dependencies
	in the tree. Our system achieves reason- able performance -- 67.87% official
	aver- aged macro F1 score},
  url       = {http://www.aclweb.org/anthology/K17-3015}
}