Entry Fujita:2013:WSD from talip.bib

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BibTeX entry

@Article{Fujita:2013:WSD,
  author =       "Sanae Fujita and Akinori Fujino",
  title =        "Word Sense Disambiguation by Combining Labeled Data
                 Expansion and Semi-Supervised Learning Method",
  journal =      j-TALIP,
  volume =       "12",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jun,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2461316.2461319",
  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 =     "Lack of labeled data is one of the severest problems
                 facing word sense disambiguation (WSD). We overcome the
                 problem by proposing a method that combines automatic
                 labeled data expansion (Step 1) and semi-supervised
                 learning (Step 2). The Step 1 and 2 methods are both
                 effective, but their combination yields a synergistic
                 effect. In this article, in Step 1, we automatically
                 extract reliable labeled data from raw corpora using
                 dictionary example sentences, even the infrequent and
                 unseen senses (which are not likely to appear in
                 labeled data). Next, in Step 2, we apply a
                 semi-supervised classifier and achieve an improvement
                 using easy-to-get unlabeled data. In this step, we also
                 show that we can guess even unseen senses. We target a
                 SemEval-2010 Japanese WSD task, which is a lexical
                 sample task. Both Step 1 and Step 2 methods performed
                 better than the best published result (76.4 \%).
                 Furthermore, the combined method achieved much higher
                 accuracy (84.2 \%). In this experiment, up to 50 \% of
                 unseen senses are classified correctly. However, the
                 number of unseen senses are small, therefore, we delete
                 one senses per word and apply our proposed method; the
                 results show that the method is effective and robust
                 even for unseen senses.",
  acknowledgement = ack-nhfb,
  articleno =    "7",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

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