Entry Iwakura:2013:NER from talip.bib

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BibTeX entry

@Article{Iwakura:2013:NER,
  author =       "Tomoya Iwakura and Hiroya Takamura and Manabu
                 Okumura",
  title =        "A Named Entity Recognition Method Based on
                 Decomposition and Concatenation of Word Chunks",
  journal =      j-TALIP,
  volume =       "12",
  number =       "3",
  pages =        "10:1--10:??",
  month =        aug,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499955.2499958",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Mon Aug 19 18:39:55 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "We propose a named entity (NE) recognition method in
                 which word chunks are repeatedly decomposed and
                 concatenated. Our method identifies word chunks with a
                 base chunker, such as a noun phrase chunker, and then
                 recognizes NEs from the recognized word chunk
                 sequences. By using word chunks, we can obtain features
                 that cannot be obtained in word-sequence-based
                 recognition methods, such as the first word of a word
                 chunk, the last word of a word chunk, and so on.
                 However, each word chunk may include a part of an NE or
                 multiple NEs. To solve this problem, we use the
                 following operators: SHIFT for separating the first
                 word from a word chunk, POP for separating the last
                 word from a word chunk, JOIN for concatenating two word
                 chunks, and REDUCE for assigning an NE label to a word
                 chunk. We evaluate our method on a Japanese NE
                 recognition dataset that includes about 200,000
                 annotations of 191 types of NEs from over 8,500 news
                 articles. The experimental results show that the
                 training and processing speeds of our method are faster
                 than those of a linear-chain structured perceptron and
                 a semi-Markov perceptron, while maintaining high
                 accuracy.",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

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