Entry Chen:2009:USD from talip.bib

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BibTeX entry

@Article{Chen:2009:USD,
  author =       "Wenliang Chen and Daisuke Kawahara and Kiyotaka
                 Uchimoto and Yujie Zhang and Hitoshi Isahara",
  title =        "Using Short Dependency Relations from Auto-Parsed Data
                 for {Chinese} Dependency Parsing",
  journal =      j-TALIP,
  volume =       "8",
  number =       "3",
  pages =        "10:1--10:??",
  month =        aug,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/1568292.1568293",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Mon Mar 29 15:37:08 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "Dependency parsing has become increasingly popular for
                 a surge of interest lately for applications such as
                 machine translation and question answering. Currently,
                 several supervised learning methods can be used for
                 training high-performance dependency parsers if
                 sufficient labeled data are available.\par

                 However, currently used statistical dependency parsers
                 provide poor results for words separated by long
                 distances. In order to solve this problem, this article
                 presents an effective dependency parsing approach of
                 incorporating short dependency information from
                 unlabeled data. The unlabeled data is automatically
                 parsed by using a deterministic dependency parser,
                 which exhibits a relatively high performance for short
                 dependencies between words. We then train another
                 parser that uses the information on short dependency
                 relations extracted from the output of the first
                 parser. The proposed approach achieves an unlabeled
                 attachment score of 86.52\%, an absolute 1.24\%
                 improvement over the baseline system on the Chinese
                 Treebank data set. The results indicate that the
                 proposed approach improves the parsing performance for
                 longer distance words.",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
  keywords =     "Chinese dependency parsing; semi-supervised learning;
                 unlabeled data",
}

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