Entry Bach:2013:TPF from talip.bib

Last update: Sun Oct 15 02:55:04 MDT 2017                Valid HTML 3.2!

Index sections

Top | Symbols | Numbers | Math | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

BibTeX entry

@Article{Bach:2013:TPF,
  author =       "Ngo Xuan Bach and Nguyen Le Minh and Tran Thi Oanh and
                 Akira Shimazu",
  title =        "A Two-Phase Framework for Learning Logical Structures
                 of Paragraphs in Legal Articles",
  journal =      j-TALIP,
  volume =       "12",
  number =       "1",
  pages =        "3:1--3:??",
  month =        mar,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2425327.2425330",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Sat Mar 2 09:25:42 MST 2013",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "Analyzing logical structures of texts is important to
                 understanding natural language, especially in the legal
                 domain, where legal texts have their own specific
                 characteristics. Recognizing logical structures in
                 legal texts does not only help people in understanding
                 legal documents, but also in supporting other tasks in
                 legal text processing. In this article, we present a
                 new task, learning logical structures of paragraphs in
                 legal articles, which is studied in research on Legal
                 Engineering. The goals of this task are recognizing
                 logical parts of law sentences in a paragraph, and then
                 grouping related logical parts into some logical
                 structures of formulas, which describe logical
                 relations between logical parts. We present a two-phase
                 framework to learn logical structures of paragraphs in
                 legal articles. In the first phase, we model the
                 problem of recognizing logical parts in law sentences
                 as a multi-layer sequence learning problem, and present
                 a CRF-based model to recognize them. In the second
                 phase, we propose a graph-based method to group logical
                 parts into logical structures. We consider the problem
                 of finding a subset of complete subgraphs in a
                 weighted-edge complete graph, where each node
                 corresponds to a logical part, and a complete subgraph
                 corresponds to a logical structure. We also present an
                 integer linear programming formulation for this
                 optimization problem. Our models achieve 74.37\% in
                 recognizing logical parts, 80.08\% in recognizing
                 logical structures, and 58.36\% in the whole task on
                 the Japanese National Pension Law corpus. Our work
                 provides promising results for further research on this
                 interesting task.",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

Related entries