Entry Higashinaka:2008:AAC from talip.bib

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BibTeX entry

@Article{Higashinaka:2008:AAC,
  author =       "Ryuichiro Higashinaka and Hideki Isozaki",
  title =        "Automatically Acquiring Causal Expression Patterns
                 from Relation-annotated Corpora to Improve Question
                 Answering for why-Questions",
  journal =      j-TALIP,
  volume =       "7",
  number =       "2",
  pages =        "6:1--6:??",
  month =        jun,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/1362782.1362785",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Mon Jun 16 17:12:23 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "This article describes our approach for answering
                 why-questions that we initially introduced at NTCIR-6
                 QAC-4. The approach automatically acquires causal
                 expression patterns from relation-annotated corpora by
                 abstracting text spans annotated with a causal relation
                 and by mining syntactic patterns that are useful for
                 distinguishing sentences annotated with a causal
                 relation from those annotated with other relations. We
                 use these automatically acquired causal expression
                 patterns to create features to represent answer
                 candidates, and use these features together with other
                 possible features related to causality to train an
                 answer candidate ranker that maximizes the QA
                 performance with regards to the corpus of why-questions
                 and answers. NAZEQA, a Japanese why-QA system based on
                 our approach, clearly outperforms baselines with a Mean
                 Reciprocal Rank (top-5) of 0.223 when sentences are
                 used as answers and with a MRR (top-5) of 0.326 when
                 paragraphs are used as answers, making it presumably
                 the best-performing fully implemented why-QA system.
                 Experimental results also verified the usefulness of
                 the automatically acquired causal expression
                 patterns.",
  acknowledgement = ack-nhfb,
  articleno =    "6",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
  keywords =     "causal expression; pattern mining; question answering;
                 relation-annotated corpus",
}

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