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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12(3)11,
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11(4)14,
11(4)18,
12(1)3,
12(2)7,
12(3)10,
12(3)11,
12(4)16,
12(4)17
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13(1)2
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8(4)17,
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7(3)8
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3(2)94,
7(1)3,
8(1)2,
8(3)12,
9(2)7,
10(4)17,
12(2)5
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3(2)94,
5(1)4,
5(2)121,
8(2)8,
8(2)9,
8(4)15,
8(4)18,
8(4)19,
10(1)4,
11(4)14,
11(4)16,
11(4)18,
12(2)7,
12(3)12,
12(4)15,
13(1)2,
13(2)9
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9(2)7,
10(4)20,
13(2)7
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5(2)89,
6(2)6,
6(4)3,
8(2)7,
9(2)7,
10(1)2,
10(1)6,
10(4)20,
11(2)6,
11(3)9,
13(3)11,
13(3)13,
13(3)14
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5(2)89,
7(1)3,
7(2)7,
7(3)8,
7(4)13,
11(2)4,
12(4)14
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7(2)6
- novel,
4(3)243,
5(2)89,
5(2)165,
6(2)8,
6(4)2,
8(1)3,
8(3)12,
10(2)7,
10(3)14,
10(4)19,
10(4)20,
11(1)3,
11(2)4,
11(4)15,
11(4)17,
12(3)11,
12(3)12,
13(2)8,
13(3)13,
13(4)17
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5(2)89,
5(2)121,
6(2)6,
6(3)11,
7(1)1,
7(3)8,
7(4)12,
8(1)3,
8(2)9,
8(4)17,
10(4)19,
10(4)21,
11(4)13,
11(4)18,
12(3)9,
12(4)14,
13(3)11
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9(3)11
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1(1)65,
5(1)44,
6(4)1,
7(2)5,
7(2)6,
7(4)12,
10(2)7,
11(1)3,
11(4)17,
12(1)2
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9(3)12,
9(4)14,
11(2)4,
11(4)16,
12(1)2,
13(4)17
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5(2)121,
5(2)165,
6(2)8,
6(3)9,
6(4)1,
6(4)3,
7(1)1,
7(1)2,
7(2)5,
7(2)6,
7(2)7,
7(3)9,
7(3)10,
7(4)13,
8(1)2,
8(1)3,
8(2)7,
8(2)8,
8(2)9,
8(3)10,
8(4)16,
8(4)17,
8(4)18,
9(1)2,
9(1)4,
9(2)5,
9(2)6,
9(3)11,
9(3)12,
9(4)14,
10(2)8,
10(3)13,
10(3)14,
11(2)7,
11(3)10,
11(4)14,
11(4)15,
11(4)17,
12(1)2,
12(3)9,
12(3)11,
12(4)14,
12(4)15,
12(4)16,
13(1)3,
13(1)4,
13(2)7,
13(2)9,
13(4)16,
13(4)17
- precisely,
9(2)5
- precision,
7(3)9,
9(3)11,
10(2)8,
11(4)18,
12(1)1,
13(2)6
- problem,
6(2)7,
6(3)9,
6(3)11,
6(4)1,
7(1)2,
7(2)7,
7(3)10,
8(1)2,
8(2)9,
8(3)10,
8(4)19,
9(1)1,
9(1)3,
9(2)5,
9(4)13,
10(1)2,
10(1)4,
10(3)14,
10(3)16,
10(4)21,
11(3)8,
11(4)17,
11(4)18,
12(1)2,
12(1)3,
12(2)7,
12(3)10,
12(3)12,
12(4)16,
13(2)8,
13(3)14,
13(4)17
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5(2)89,
6(2)6,
6(2)8,
6(3)11,
7(3)8,
7(3)10,
7(4)12,
8(1)2,
8(1)4,
8(2)9,
8(4)19,
9(2)7,
9(4)13,
10(2)10,
10(3)12,
10(3)15,
10(4)17,
10(4)20,
11(2)7,
11(3)9,
11(4)15,
11(4)16,
11(4)18,
12(1)1,
12(1)2,
12(1)3,
12(1)4,
12(2)5,
12(2)6,
12(3)9,
12(3)10,
12(3)12,
12(4)16,
13(1)2,
13(1)3,
13(2)8,
13(2)9,
13(3)12,
13(3)13,
13(4)17,
13(4)18
- proposed,
5(2)121,
5(2)165,
6(2)7,
7(1)1,
7(1)2,
7(3)9,
7(3)10,
7(4)11,
7(4)13,
8(1)4,
8(2)6,
8(3)10,
8(3)11,
8(4)14,
8(4)19,
9(1)1,
9(2)5,
9(2)7,
10(2)7,
10(2)9,
10(3)14,
10(4)18,
11(1)3,
11(2)5,
11(2)6,
11(2)7,
11(3)8,
11(3)9,
11(3)10,
11(4)16,
11(4)17,
12(1)4,
12(2)5,
12(2)7,
12(3)12,
12(4)16,
12(4)17,
13(2)6,
13(2)8,
13(3)13,
13(4)18
- query,
1(2)123,
1(2)159,
4(2)57,
5(3)245,
5(4)323,
5(4)360,
6(2)8,
6(4)2,
7(2)5,
9(1)1,
9(3)12,
11(2)6,
11(4)17,
13(2)6,
13(3)13
- rank,
7(2)6,
9(1)1,
10(1)3
- ranking,
7(4)12,
8(1)3,
10(1)3,
12(1)2
- reasonable,
7(1)2,
9(4)14
- recognize,
7(4)13,
11(4)14,
11(4)15,
12(1)3,
12(3)10
- relation,
1(3)173,
7(2)6,
7(4)12,
8(3)10,
9(2)7,
10(3)14,
10(3)15,
11(1)3,
11(4)16,
11(4)18,
12(1)3,
13(1)2,
13(1)3,
13(1)4
- represent,
5(2)146,
5(2)165,
7(2)6,
7(4)11,
8(1)4,
11(2)5
- result,
4(2)135,
5(2)121,
5(2)146,
5(2)165,
6(2)6,
6(2)7,
6(3)9,
6(3)11,
6(4)3,
7(1)2,
7(2)5,
7(2)6,
7(2)7,
7(3)8,
7(3)10,
7(4)11,
7(4)12,
7(4)13,
8(1)2,
8(1)3,
8(1)4,
8(2)6,
8(2)9,
8(3)10,
8(3)12,
8(4)14,
8(4)15,
8(4)16,
8(4)17,
8(4)18,
8(4)19,
9(1)1,
9(1)2,
9(2)5,
9(2)6,
9(2)7,
9(3)11,
9(3)12,
9(4)14,
10(1)2,
10(2)7,
11(2)4,
11(2)5,
11(3)8,
11(3)9,
11(4)13,
11(4)14,
11(4)15,
12(1)3,
12(1)4,
12(2)5,
12(2)7,
12(3)9,
12(3)10,
12(3)11,
12(4)14,
12(4)16,
13(1)1,
13(1)4,
13(2)6,
13(2)7,
13(2)9,
13(3)11,
13(3)12,
13(3)14
- retrieval,
1(3)225,
1(4)281,
2(1)1,
2(2)164,
3(2)128,
4(1)1,
4(2)57,
4(2)78,
4(2)135,
4(2)186,
4(3)340,
4(3)357,
4(4)377,
4(4)475,
5(2)89,
5(3)264,
5(4)296,
5(4)323,
6(2)8,
6(4)2,
7(2)5,
7(3)8,
7(4)12,
8(1)1,
8(1)2,
9(1)1,
9(3)9,
9(3)10,
9(3)11,
9(3)12,
9(4)13,
9(4)14,
10(2)8,
10(4)20,
11(1)2,
13(2)6,
13(2)7,
13(3)13,
13(4)18
- retrieve,
6(2)8,
9(1)1
- search,
2(3)219,
4(2)159,
4(3)280,
6(2)8,
6(4)2,
7(3)8,
8(1)2,
8(2)8,
8(3)12,
9(1)1,
9(3)11,
10(2)9,
10(3)16,
11(3)8
- second,
5(2)89,
8(1)4,
8(2)7,
8(4)19,
11(1)3,
11(2)6,
11(3)8,
11(3)9,
12(1)3,
13(2)7,
13(4)17
- semantic,
3(1)66,
3(2)94,
5(2)165,
5(3)228,
7(1)2,
7(1)3,
7(4)13,
8(1)2,
8(2)6,
8(3)12,
8(4)15,
9(2)7,
10(1)5,
10(3)13,
10(3)14,
10(3)15,
10(4)20,
10(4)21,
11(3)8,
11(4)15,
11(4)18,
12(3)11,
12(4)16
- sentence,
1(3)173,
3(2)146,
4(3)321,
4(4)377,
5(2)121,
5(2)146,
5(2)165,
7(1)3,
7(2)6,
7(4)13,
8(1)3,
8(2)8,
9(1)2,
9(2)6,
10(4)21,
11(1)3,
11(2)5,
11(2)6,
11(3)8,
11(3)10,
12(1)2,
12(1)3,
12(2)7,
12(4)14,
12(4)17,
13(1)2,
13(3)11,
13(4)17
- set,
1(3)269,
5(2)121,
6(1)z,
7(1)3,
7(3)8,
7(4)11,
7(4)13,
8(3)10,
8(3)12,
8(4)15,
9(1)1,
9(1)3,
9(2)5,
10(1)4,
10(2)8,
10(4)20,
11(2)5,
11(2)7,
11(3)10,
11(4)13,
11(4)14,
12(1)2,
12(1)4,
12(3)9,
13(2)8,
13(2)9,
13(3)12,
13(3)13,
13(4)17
- show,
5(2)89,
5(2)146,
7(1)1,
7(1)2,
7(1)3,
7(4)11,
7(4)12,
7(4)13,
8(1)4,
8(2)7,
8(2)9,
8(3)12,
8(4)16,
8(4)17,
9(1)1,
9(1)2,
9(1)3,
9(2)5,
9(2)6,
9(2)7,
9(3)11,
9(3)12,
9(4)14,
10(1)3,
10(3)15,
11(2)4,
11(2)5,
11(2)7,
11(3)8,
11(4)14,
11(4)15,
11(4)17,
11(4)18,
12(1)2,
12(1)4,
12(2)5,
12(2)7,
12(3)9,
12(3)10,
12(3)11,
12(4)15,
12(4)16,
13(1)3,
13(2)6,
13(2)7,
13(2)9,
13(3)14
- similar,
5(2)146,
7(3)8,
7(4)11,
8(3)12,
9(3)11,
9(3)12,
10(1)2,
10(2)10,
11(2)6,
12(1)2,
12(4)17,
13(2)8
- similarity,
5(2)89,
5(2)165,
6(1)z-1,
6(2)6,
7(3)8,
8(2)6,
8(2)9,
9(1)1,
10(2)10,
11(2)5,
11(2)6,
11(3)9
- speed,
6(2)8,
12(2)5,
12(2)6,
12(3)10,
12(3)12
- storing,
6(3)9
- study,
4(2)159,
4(3)243,
5(2)121,
5(2)146,
5(2)165,
5(3)209,
6(2)6,
6(2)7,
8(1)3,
8(1)4,
8(4)16,
9(2)5,
9(2)6,
9(2)7,
9(3)11,
10(2)10,
10(3)12,
10(4)17,
10(4)18,
11(1)3,
11(2)6,
11(3)9,
11(4)14,
13(1)3,
13(2)7,
13(3)11,
13(3)12,
13(3)14
- such,
7(2)7,
7(3)8,
7(3)10,
7(4)12,
8(2)8,
8(3)10,
8(3)11,
8(3)12,
8(4)14,
8(4)16,
8(4)17,
9(1)1,
9(3)12,
9(4)13,
9(4)15,
10(1)5,
10(2)8,
10(3)12,
10(4)21,
11(1)2,
11(2)5,
11(2)7,
11(3)8,
11(3)10,
11(4)13,
11(4)16,
11(4)17,
11(4)18,
12(1)1,
12(1)2,
12(2)6,
12(3)10,
12(3)11,
12(4)14,
12(4)17,
13(1)1,
13(3)12,
13(4)17
- supporting,
12(1)3,
13(1)1
- task,
2(1)49,
5(2)89,
5(2)121,
6(2)7,
6(3)11,
6(4)1,
6(4)3,
7(1)1,
7(1)2,
7(2)7,
7(3)10,
7(4)13,
8(1)4,
8(2)7,
8(4)15,
8(4)16,
9(1)4,
9(2)6,
9(3)10,
9(4)14,
9(4)15,
10(1)5,
10(3)14,
10(4)18,
10(4)20,
10(4)21,
11(1)2,
11(3)8,
11(4)13,
11(4)14,
11(4)17,
11(4)18,
12(1)2,
12(1)3,
12(2)5,
12(2)7,
12(3)9,
12(4)17,
13(2)10,
13(4)17
- then,
5(2)121,
6(2)6,
7(1)1,
7(3)10,
7(4)12,
8(1)4,
8(2)7,
8(3)10,
8(3)11,
8(3)12,
8(4)14,
9(1)1,
9(2)7,
9(3)11,
10(2)7,
10(3)13,
10(4)20,
11(1)3,
11(2)7,
11(4)15,
12(1)3,
12(3)10,
12(4)17,
13(1)4,
13(2)9,
13(3)13,
13(4)16
- thereby,
12(4)16
- therefore,
7(1)2,
8(4)16,
8(4)17,
11(2)5,
11(2)6,
11(3)8,
11(4)13,
12(2)7,
12(4)16,
13(2)8
- two,
5(2)89,
7(2)7,
7(3)8,
7(4)11,
7(4)12,
7(4)13,
8(1)4,
8(2)7,
8(4)17,
9(1)2,
9(3)11,
9(4)13,
10(1)2,
10(3)12,
10(3)14,
10(3)15,
10(4)20,
11(2)4,
11(2)5,
11(2)7,
11(3)8,
11(3)9,
11(4)17,
12(1)1,
12(1)2,
12(1)4,
12(2)5,
12(3)10,
12(3)11,
12(4)16,
13(1)1,
13(1)3,
13(1)4,
13(2)6,
13(2)9,
13(3)11,
13(4)17
- Web,
1(2)159,
3(1)66,
6(2)6,
7(3)8,
7(4)11,
8(3)12,
9(1)1,
9(4)15,
10(4)21,
11(2)4,
11(4)16
- well,
5(2)121,
6(2)6,
6(3)11,
7(3)8,
7(3)9,
7(3)10,
8(1)2,
8(4)18,
9(1)2,
9(3)12,
10(3)15,
11(1)2,
11(2)4,
12(3)9,
13(1)2