Entry Sun:2013:LAC from talip.bib

Last update: Sun Oct 15 02:55:04 MDT 2017                Valid HTML 3.2!

Index sections

Top | Symbols | Numbers | Math | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

BibTeX entry

@Article{Sun:2013:LAC,
  author =       "Xu Sun and Naoaki Okazaki and Jun'ichi Tsujii and
                 Houfeng Wang",
  title =        "Learning Abbreviations from {Chinese} and {English}
                 Terms by Modeling Non-Local Information",
  journal =      j-TALIP,
  volume =       "12",
  number =       "2",
  pages =        "5:1--5:??",
  month =        jun,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2461316.2461317",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Thu Jun 6 06:48:55 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "The present article describes a robust approach for
                 abbreviating terms. First, in order to incorporate
                 non-local information into abbreviation generation
                 tasks, we present both implicit and explicit solutions:
                 the latent variable model and the label encoding with
                 global information. Although the two approaches compete
                 with one another, we find they are also highly
                 complementary. We propose a combination of the two
                 approaches, and we will show the proposed method
                 outperforms all of the existing methods on abbreviation
                 generation datasets. In order to reduce computational
                 complexity of learning non-local information, we
                 further present an online training method, which can
                 arrive the objective optimum with accelerated training
                 speed. We used a Chinese newswire dataset and a English
                 biomedical dataset for experiments. Experiments
                 revealed that the proposed abbreviation generator with
                 non-local information achieved the best results for
                 both the Chinese and English languages.",
  acknowledgement = ack-nhfb,
  articleno =    "5",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

Related entries