Entry Carpuat:2006:AWS 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{Carpuat:2006:AWS,
  author =       "Marine Carpuat and Pascale Fung and Grace Ngai",
  title =        "Aligning word senses using bilingual corpora",
  journal =      j-TALIP,
  volume =       "5",
  number =       "2",
  pages =        "89--120",
  month =        jun,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/1165255.1165256",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Thu Oct 5 07:00:29 MDT 2006",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "The growing importance of multilingual information
                 retrieval and machine translation has made multilingual
                 ontologies extremely valuable resources. Since the
                 construction of an ontology from scratch is a very
                 expensive and time-consuming undertaking, it is
                 attractive to consider ways of automatically aligning
                 monolingual ontologies, which already exist for many of
                 the world's major languages. Previous research
                 exploited similarity in the structure of the ontologies
                 to align, or manually created bilingual resources.
                 These approaches cannot be used to align ontologies
                 with vastly different structures and can only be
                 applied to much studied language pairs for which
                 expensive resources are already available. In this
                 paper, we propose a novel approach to align the
                 ontologies at the node level: Given a concept
                 represented by a particular word sense in one ontology,
                 our task is to find the best corresponding word sense
                 in the second language ontology. To this end, we
                 present a language-independent, corpus-based method
                 that borrows from techniques used in information
                 retrieval and machine translation. We show its
                 efficiency by applying it to two very different
                 ontologies in very different languages: the Mandarin
                 Chinese HowNet and the American English WordNet.
                 Moreover, we propose a methodology to measure bilingual
                 corpora comparability and show that our method is
                 robust enough to use noisy nonparallel bilingual
                 corpora efficiently, when clean parallel corpora are
                 not available.",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

Related entries