Entry Xiao:2007:SNM from talip.bib

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BibTeX entry

@Article{Xiao:2007:SNM,
  author =       "Jinghui Xiao and Xiaolong Wang and Bingquan Liu",
  title =        "The study of a nonstationary maximum entropy {Markov}
                 model and its application on the pos-tagging task",
  journal =      j-TALIP,
  volume =       "6",
  number =       "2",
  pages =        "7:1--7:??",
  month =        sep,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/1282080.1282082",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Mon Jun 16 17:11:28 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "Sequence labeling is a core task in natural language
                 processing. The maximum entropy Markov model (MEMM) is
                 a powerful tool in performing this task. This article
                 enhances the traditional MEMM by exploiting the
                 positional information of language elements. The
                 stationary hypothesis is relaxed in MEMM, and the
                 nonstationary MEMM (NS-MEMM) is proposed. Several
                 related issues are discussed in detail, including the
                 representation of positional information, NS-MEMM
                 implementation, smoothing techniques, and the space
                 complexity issue. Furthermore, the asymmetric NS-MEMM
                 presents a more flexible way to exploit positional
                 information. In the experiments, NS-MEMM is evaluated
                 on both the Chinese and the English pos-tagging tasks.
                 According to the experimental results, NS-MEMM yields
                 effective improvements over MEMM by exploiting
                 positional information. The smoothing techniques in
                 this article effectively solve the NS-MEMM
                 data-sparseness problem; the asymmetric NS-MEMM is also
                 an improvement by exploiting positional information in
                 a more flexible way.",
  acknowledgement = ack-nhfb,
  articleno =    "7",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
  keywords =     "data sparseness problem; Markov property; MEMM;
                 pos-tagging; stationary hypothesis",
}

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