@inproceedings{qian-liu-2017-non,
    title = "A non-{DNN} Feature Engineering Approach to Dependency Parsing {--} {FBAML} at {C}o{NLL} 2017 Shared Task",
    author = "Qian, Xian  and
      Liu, Yang",
    editor = "Haji{\v{c}}, Jan  and
      Zeman, Dan",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/K17-3015/",
    doi = "10.18653/v1/K17-3015",
    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 reasonable performance {--} 67.87{\%} official averaged macro F1 score"
}