@article{sun-wan-2013-data,
    title = "Data-driven, {PCFG}-based and Pseudo-{PCFG}-based Models for {C}hinese Dependency Parsing",
    author = "Sun, Weiwei  and
      Wan, Xiaojun",
    editor = "Lin, Dekang  and
      Collins, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "1",
    year = "2013",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest_wac_2008/Q13-1025/",
    doi = "10.1162/tacl_a_00229",
    pages = "301--314",
    abstract = "We present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models. Inspired by the impact of a constituency grammar on dependency parsing, we propose several strategies to acquire pseudo CFGs only from dependency annotations. Compared to linguistic grammars learned from rich phrase-structure treebanks, well designed pseudo grammars achieve similar parsing accuracy and have equivalent contributions to parser ensemble. Moreover, pseudo grammars increase the diversity of base models; therefore, together with all other models, further improve system combination. Based on automatic POS tagging, our final model achieves a UAS of 87.23{\%}, resulting in a significant improvement of the state of the art."
}