Entry Miyao:2012:ETE from talip.bib

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BibTeX entry

@Article{Miyao:2012:ETE,
  author =       "Yusuke Miyao and Hideki Shima and Hiroshi Kanayama and
                 Teruko Mitamura",
  title =        "Evaluating Textual Entailment Recognition for
                 University Entrance Examinations",
  journal =      j-TALIP,
  volume =       "11",
  number =       "4",
  pages =        "13:1--13:??",
  month =        dec,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2382593.2382595",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Thu Dec 6 07:40:55 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "The present article addresses an attempt to apply
                 questions in university entrance examinations to the
                 evaluation of textual entailment recognition. Questions
                 in several fields, such as history and politics,
                 primarily test the examinee's knowledge in the form of
                 choosing true statements from multiple choices.
                 Answering such questions can be regarded as equivalent
                 to finding evidential texts from a textbase such as
                 textbooks and Wikipedia. Therefore, this task can be
                 recast as recognizing textual entailment between a
                 description in a textbase and a statement given in a
                 question. We focused on the National Center Test for
                 University Admission in Japan and converted questions
                 into the evaluation data for textual entailment
                 recognition by using Wikipedia as a textbase.
                 Consequently, it is revealed that nearly half of the
                 questions can be mapped into textual entailment
                 recognition; 941 text pairs were created from 404
                 questions from six subjects. This data set is provided
                 for a subtask of NTCIR RITE (Recognizing Inference in
                 Text), and 16 systems from six teams used the data set
                 for evaluation. The evaluation results revealed that
                 the best system achieved a correct answer ratio of
                 56\%, which is significantly better than a random
                 choice baseline.",
  acknowledgement = ack-nhfb,
  articleno =    "13",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

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