Entry Fukumoto:2007:TTB from talip.bib
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BibTeX entry
@Article{Fukumoto:2007:TTB,
author = "Fumiyo Fukumoto and Yoshimi Suzuki",
title = "Topic tracking based on bilingual comparable corpora
and semisupervised clustering",
journal = j-TALIP,
volume = "6",
number = "3",
pages = "11:1--11:??",
month = nov,
year = "2007",
CODEN = "????",
DOI = "https://doi.org/10.1145/1290002.1290005",
ISSN = "1530-0226 (print), 1558-3430 (electronic)",
ISSN-L = "1530-0226",
bibdate = "Mon Jun 16 17:11:45 MDT 2008",
bibsource = "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/talip.bib",
abstract = "In this paper, we address the problem of skewed data
in topic tracking: the small number of stories labeled
positive as compared to negative stories and propose a
method for estimating effective training stories for
the topic-tracking task. For a small number of labeled
positive stories, we use bilingual comparable, i.e.,
English, and Japanese corpora, together with the EDR
bilingual dictionary, and extract story pairs
consisting of positive and associated stories. To
overcome the problem of a large number of labeled
negative stories, we classified them into clusters.
This is done using a semisupervised clustering
algorithm, combining $k$ means with EM. The method was
tested on the TDT English corpus and the results showed
that the system works well when the topic under
tracking is talking about an event originating in the
source language country, even for a small number of
initial positive training stories.",
acknowledgement = ack-nhfb,
articleno = "11",
fjournal = "ACM Transactions on Asian Language Information
Processing",
journal-URL = "http://portal.acm.org/browse_dl.cfm?&idx=J820",
keywords = "bilingual comparable corpora; clustering; EM
algorithm; N-gram model; topic detection and tracking",
}
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9(2)5,
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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(3)11,
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,
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12(4)16,
13(1)1,
13(1)4,
13(2)6,
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13(2)9,
13(3)11,
13(3)12,
13(3)14
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7(3)8,
10(4)18,
11(3)9,
12(4)17,
13(2)6,
13(4)16
- small,
7(3)9,
7(4)11,
8(2)9,
8(4)17,
9(1)3,
10(4)21,
12(2)7
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5(2)121,
8(2)6,
8(2)7,
8(2)8,
8(4)17,
9(1)1,
9(4)15,
10(3)12,
10(4)17,
10(4)19,
11(4)18,
12(4)15,
13(1)1,
13(1)2,
13(3)12,
13(4)17
- Suzuki, Yoshimi,
7(3)8
- task,
2(1)49,
5(2)89,
5(2)121,
6(2)7,
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,
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11(1)2,
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8(1)2
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9(1)3,
11(2)7,
12(1)4,
13(2)6,
13(4)16
- together,
7(2)6,
7(3)8,
8(3)12,
9(3)11,
10(3)12,
12(3)9
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2(2)85,
3(4)227,
5(4)388,
8(1)2,
8(3)12,
9(2)7,
9(3)12,
9(4)15,
11(2)4,
11(2)5
- tracking,
2(2)85,
3(4)227,
5(4)388
- training,
5(2)121,
7(1)1,
8(1)3,
8(2)6,
8(2)7,
8(3)10,
9(2)5,
10(3)12,
10(3)13,
11(3)9,
12(1)1,
12(2)5,
12(3)9,
12(3)10,
13(1)2,
13(1)3,
13(1)4,
13(4)17
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4(2)159,
5(2)89,
5(2)146,
6(2)8,
7(2)6,
7(3)9,
7(4)11,
7(4)12,
8(1)3,
8(2)9,
8(3)10,
8(3)11,
9(1)1,
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9(3)11,
10(1)3,
10(1)4,
11(1)1,
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11(2)7,
11(3)8,
11(3)10,
11(4)14,
11(4)18,
12(1)1,
12(2)6,
12(3)9,
12(3)10,
13(2)6,
13(2)9,
13(2)10,
13(3)12
- was,
5(2)146,
6(3)9,
8(4)18,
8(4)19,
9(2)7,
9(3)10,
10(4)18,
11(1)2,
11(3)10,
12(4)14,
13(1)4,
13(4)16
- well,
5(2)121,
6(2)6,
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,
11(3)11,
12(3)9,
13(1)2
- when,
5(2)89,
6(3)9,
7(2)6,
7(4)11,
7(4)13,
8(1)3,
8(4)16,
8(4)17,
9(1)3,
9(2)5,
9(3)11,
9(3)12,
9(4)13,
11(2)4,
12(1)2,
12(4)14,
13(2)8,
13(3)12
- work,
5(2)121,
6(2)6,
6(4)2,
7(2)7,
7(3)9,
8(4)19,
9(2)5,
9(4)15,
10(1)4,
10(2)10,
12(1)3,
12(3)9,
13(1)1,
13(2)9,
13(3)14,
13(4)18