An improved algorithm for weighting keywords in web documents

Shuang Sun; Liang He; Jing Yang; Jun Zhong Gu

doi:10.1007/s11741-008-0309-2

An improved algorithm for weighting keywords in web documents

Shuang Sun^*, Liang He, Jing Yang, Jun Zhong Gu

^*Corresponding author for this work

School of Computer Science and Technology

East China Normal University

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, an improved algorithm, web-based keyword weight algorithm (WKWA), is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF*IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model (IVSM). A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.

Original language	English
Pages (from-to)	235-239
Number of pages	5
Journal	Journal of Shanghai University
Volume	12
Issue number	3
DOIs	https://doi.org/10.1007/s11741-008-0309-2
State	Published - Jun 2008

Keywords

Feature item
Improved vector space model (IVSM)
Keyword weight
Representation feature
Semantic similarity

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10.1007/s11741-008-0309-2

Cite this

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title = "An improved algorithm for weighting keywords in web documents",

abstract = "In this paper, an improved algorithm, web-based keyword weight algorithm (WKWA), is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF*IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model (IVSM). A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.",

keywords = "Feature item, Improved vector space model (IVSM), Keyword weight, Representation feature, Semantic similarity",

author = "Shuang Sun and Liang He and Jing Yang and Gu, \{Jun Zhong\}",

year = "2008",

month = jun,

doi = "10.1007/s11741-008-0309-2",

language = "英语",

volume = "12",

pages = "235--239",

journal = "Journal of Shanghai University",

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TY - JOUR

T1 - An improved algorithm for weighting keywords in web documents

AU - Sun, Shuang

AU - He, Liang

AU - Yang, Jing

AU - Gu, Jun Zhong

PY - 2008/6

Y1 - 2008/6

N2 - In this paper, an improved algorithm, web-based keyword weight algorithm (WKWA), is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF*IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model (IVSM). A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.

AB - In this paper, an improved algorithm, web-based keyword weight algorithm (WKWA), is presented to weight keywords in web documents. WKWA takes into account representation features of web documents and advantages of the TF*IDF, TFC and ITC algorithms in order to make it more appropriate for web documents. Meanwhile, the presented algorithm is applied to improved vector space model (IVSM). A real system has been implemented for calculating semantic similarities of web documents. Four experiments have been carried out. They are keyword weight calculation, feature item selection, semantic similarity calculation, and WKWA time performance. The results demonstrate accuracy of keyword weight, and semantic similarity is improved.

KW - Feature item

KW - Improved vector space model (IVSM)

KW - Keyword weight

KW - Representation feature

KW - Semantic similarity

UR - https://www.scopus.com/pages/publications/45749113383

U2 - 10.1007/s11741-008-0309-2

DO - 10.1007/s11741-008-0309-2

M3 - 文章

AN - SCOPUS:45749113383

SN - 1007-6417

VL - 12

SP - 235

EP - 239

JO - Journal of Shanghai University

JF - Journal of Shanghai University

IS - 3

ER -

An improved algorithm for weighting keywords in web documents

Abstract

Keywords

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