Entry Li:2008:ASV from talip.bib

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BibTeX entry

@Article{Li:2008:ASV,
  author =       "Yaoyong Li and Kalina Bontcheva",
  title =        "Adapting Support Vector Machines for ${F}$-term-based
                 Classification of Patents",
  journal =      j-TALIP,
  volume =       "7",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jun,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/1362782.1362786",
  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 =     "Support Vector Machines (SVM) have obtained
                 state-of-the-art results on many applications including
                 document classification. However, previous works on
                 applying SVMs to the $F$-term patent classification
                 task did not obtain as good results as other learning
                 algorithms such as k-NN. This is due to the fact that
                 $F$-term patent classification is different from
                 conventional document classification in several
                 aspects, mainly because it is a multiclass, multilabel
                 classification problem with semi-structured documents
                 and multi-faceted hierarchical categories.\par

                 This article describes our SVM-based system and several
                 techniques we developed successfully to adapt SVM for
                 the specific features of the $F$-term patent
                 classification task. We evaluate the techniques using
                 the NTCIR-6 $F$-term classification terms assigned to
                 Japanese patents. Moreover, our system participated in
                 the NTCIR-6 patent classification evaluation and
                 obtained the best results according to two of the three
                 metrics used for task performance evaluation. Following
                 the NTCIR-6 participation, we developed two new
                 techniques, which achieved even better scores using all
                 three NTCIR-6 metrics, effectively outperforming all
                 participating systems. This article presents this new
                 work and the experimental results that demonstrate the
                 benefits of the latest approach.",
  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 =     "F-term classification; patent processing; support
                 vector machines",
}

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