Entry Duc:2012:CLL from talip.bib

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BibTeX entry

@Article{Duc:2012:CLL,
  author =       "Nguyen Tuan Duc and Danushka Bollegala and Mitsuru
                 Ishizuka",
  title =        "Cross-Language Latent Relational Search between
                 {Japanese} and {English} Languages Using a {Web}
                 Corpus",
  journal =      j-TALIP,
  volume =       "11",
  number =       "3",
  pages =        "11:1--11:??",
  month =        sep,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2334801.2334805",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Tue Sep 11 14:17:04 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "Latent relational search is a novel entity retrieval
                 paradigm based on the proportional analogy between two
                 entity pairs. Given a latent relational search query
                 {(Japan, Tokyo), (France, ?)}, a latent relational
                 search engine is expected to retrieve and rank the
                 entity ``Paris'' as the first answer in the result
                 list. A latent relational search engine extracts
                 entities and relations between those entities from a
                 corpus, such as the Web. Moreover, from some supporting
                 sentences in the corpus, (e.g., ``Tokyo is the capital
                 of Japan'' and ``Paris is the capital and biggest city
                 of France''), the search engine must recognize the
                 relational similarity between the two entity pairs. In
                 cross-language latent relational search, the entity
                 pairs as well as the supporting sentences of the first
                 entity pair and of the second entity pair are in
                 different languages. Therefore, the search engine must
                 recognize similar semantic relations across languages.
                 In this article, we study the problem of cross-language
                 latent relational search between Japanese and English
                 using Web data. To perform cross-language latent
                 relational search in high speed, we propose a
                 multi-lingual indexing method for storing entities and
                 lexical patterns that represent the semantic relations
                 extracted from Web corpora. We then propose a hybrid
                 lexical pattern clustering algorithm to capture the
                 semantic similarity between lexical patterns across
                 languages. Using this algorithm, we can precisely
                 measure the relational similarity between entity pairs
                 across languages, thereby achieving high precision in
                 the task of cross-language latent relational search.
                 Experiments show that the proposed method achieves an
                 MRR of 0.605 on Japanese-English cross-language latent
                 relational search query sets and it also achieves a
                 reasonable performance on the INEX Entity Ranking
                 task.",
  acknowledgement = ack-nhfb,
  articleno =    "11",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

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