Entry Che:2008:UHC from talip.bib

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BibTeX entry

@Article{Che:2008:UHC,
  author =       "Wanxiang Che and Min Zhang and AiTi Aw and ChewLim Tan
                 and Ting Liu and Sheng Li",
  title =        "Using a Hybrid Convolution Tree Kernel for Semantic
                 Role Labeling",
  journal =      j-TALIP,
  volume =       "7",
  number =       "4",
  pages =        "13:1--13:??",
  month =        nov,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/1450295.1450298",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Mon Dec 8 13:56:10 MST 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "As a kind of Shallow Semantic Parsing, Semantic Role
                 Labeling (SRL) is gaining more attention as it benefits
                 a wide range of natural language processing
                 applications. Given a sentence, the task of SRL is to
                 recognize semantic arguments (roles) for each predicate
                 (target verb or noun). Feature-based methods have
                 achieved much success in SRL and are regarded as the
                 state-of-the-art methods for SRL. However, these
                 methods are less effective in modeling structured
                 features. As an extension of feature-based methods,
                 kernel-based methods are able to capture structured
                 features more efficiently in a much higher dimension.
                 Application of kernel methods to SRL has been achieved
                 by selecting the tree portion of a predicate and one of
                 its arguments as feature space, which is named as
                 predicate-argument feature (PAF) kernel. The PAF kernel
                 captures the syntactic tree structure features using
                 convolution tree kernel, however, it does not
                 distinguish between the path structure and the
                 constituent structure. In this article, a hybrid
                 convolution tree kernel is proposed to model different
                 linguistic objects. The hybrid convolution tree kernel
                 consists of two individual convolution tree kernels.
                 They are a Path kernel, which captures
                 predicate-argument link features, and a Constituent
                 Structure kernel, which captures the syntactic
                 structure features of arguments. Evaluations on the
                 data sets of the CoNLL-2005 SRL shared task and the
                 Chinese PropBank (CPB) show that our proposed hybrid
                 convolution tree kernel statistically significantly
                 outperforms the previous tree kernels. Moreover, in
                 order to maximize the system performance, we present a
                 composite kernel through combining our hybrid
                 convolution tree kernel method with a feature-based
                 method extended by the polynomial kernel. The
                 experimental results show that the composite kernel
                 achieves better performance than each of the individual
                 methods and outperforms the best reported system on the
                 CoNLL-2005 corpus when only one syntactic parser is
                 used and on the CPB corpus when automated syntactic
                 parse results and correct syntactic parse results are
                 used respectively.",
  acknowledgement = ack-nhfb,
  articleno =    "13",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
  keywords =     "hybrid convolution tree kernel; semantic role
                 labeling",
}

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