Entry Nguyen:2009:WSC from talip.bib
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BibTeX entry
@Article{Nguyen:2009:WSC,
author = "Cam-Tu Nguyen and Xuan-Hieu Phan and Susumu Horiguchi
and Thu-Trang Nguyen and Quang-Thuy Ha",
title = "{Web} Search Clustering and Labeling with Hidden
Topics",
journal = j-TALIP,
volume = "8",
number = "3",
pages = "12:1--12:??",
month = aug,
year = "2009",
CODEN = "????",
DOI = "https://doi.org/10.1145/1568292.1568295",
ISSN = "1530-0226 (print), 1558-3430 (electronic)",
ISSN-L = "1530-0226",
bibdate = "Mon Mar 29 15:37:08 MDT 2010",
bibsource = "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/talip.bib",
abstract = "Web search clustering is a solution to reorganize
search results (also called ``snippets'') in a more
convenient way for browsing. There are three key
requirements for such post-retrieval clustering
systems: (1) the clustering algorithm should group
similar documents together; (2) clusters should be
labeled with descriptive phrases; and (3) the
clustering system should provide high-quality
clustering without downloading the whole Web
page.\par
This article introduces a novel framework for
clustering Web search results in Vietnamese which
targets the three above issues. The main motivation is
that by enriching short snippets with hidden topics
from huge resources of documents on the Internet, it is
able to cluster and label such snippets effectively in
a topic-oriented manner without concerning whole Web
pages. Our approach is based on recent successful topic
analysis models, such as Probabilistic-Latent Semantic
Analysis, or Latent Dirichlet Allocation. The
underlying idea of the framework is that we collect a
very large external data collection called ``universal
dataset,'' and then build a clustering system on both
the original snippets and a rich set of hidden topics
discovered from the universal data collection. This can
be seen as a richer representation of snippets to be
clustered. We carry out careful evaluation of our
method and show that our method can yield impressive
clustering quality.",
acknowledgement = ack-nhfb,
articleno = "12",
fjournal = "ACM Transactions on Asian Language Information
Processing",
journal-URL = "http://portal.acm.org/browse_dl.cfm?&idx=J820",
keywords = "cluster labeling; collocation; hidden topics analysis;
Hierarchical Agglomerative Clustering; Latent Dirichlet
allocation; Vietnamese; Web search clustering",
}
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12(4)17,
13(1)4,
13(2)9,
13(3)13,
13(4)16
- there,
7(3)9,
7(4)11,
8(2)7,
8(4)17,
9(2)5,
9(3)12,
9(4)15,
10(1)2,
10(3)14,
12(1)2,
13(1)1,
13(2)8,
13(3)14
- three,
1(2)145,
5(2)165,
7(2)7,
7(3)8,
8(1)4,
9(2)5,
9(2)6,
9(3)11,
10(1)5,
10(4)18,
11(4)17,
13(3)11,
13(4)16
- together,
6(3)11,
7(2)6,
7(3)8,
9(3)11,
10(3)12,
12(3)9
- topic,
2(2)85,
3(4)227,
5(4)388,
6(3)11,
8(1)2,
9(2)7,
9(3)12,
9(4)15,
11(2)4,
11(2)5
- underlying,
8(1)2,
9(1)3,
10(2)9,
10(3)12
- very,
5(2)89,
7(4)11,
8(4)16,
8(4)18,
10(1)4,
10(4)18,
12(3)12
- Vietnamese,
6(4)3,
13(1)1
- way,
5(2)89,
6(2)7,
8(4)14,
9(2)5,
10(4)17
- Web,
1(2)159,
3(1)66,
6(2)6,
7(3)8,
7(4)11,
9(1)1,
9(4)15,
10(4)21,
11(2)4,
11(3)11,
11(4)16
- which,
5(2)89,
5(2)121,
6(2)6,
6(2)8,
6(3)10,
6(4)1,
7(1)3,
7(2)7,
7(3)10,
7(4)13,
8(1)2,
8(1)4,
8(2)9,
8(3)10,
8(4)14,
8(4)16,
8(4)18,
9(1)1,
9(2)5,
9(3)12,
9(4)14,
9(4)15,
10(1)4,
10(1)6,
10(2)7,
10(2)8,
10(2)9,
10(3)12,
10(3)13,
10(3)14,
10(3)15,
10(4)17,
10(4)19,
10(4)20,
11(2)6,
11(3)8,
11(3)9,
11(4)13,
11(4)14,
11(4)15,
11(4)16,
11(4)18,
12(1)1,
12(1)3,
12(1)4,
12(2)5,
12(2)6,
12(2)7,
12(3)10,
12(3)11,
12(4)15,
12(4)17,
13(1)1,
13(2)8,
13(3)13,
13(4)17,
13(4)18
- whole,
8(1)2,
12(1)3
- yield,
6(2)7,
9(3)12,
12(2)7