Evaluating the effectiveness of the vector space retrieval model indexing

Jung Hoon Shin; Mesfin Abebe; Cheol Jung Yoo; Suntae Kim; Jeong Hyu Lee; Hee Kyung Yoo

doi:10.1007/978-981-10-3023-9_104

Evaluating the effectiveness of the vector space retrieval model indexing

Jung Hoon Shin
, Mesfin Abebe
, Cheol Jung Yoo
, Suntae Kim^*
, Jeong Hyu Lee
, Hee Kyung Yoo

^*Corresponding author for this work

Jeonbuk National University
Adama Sience and Technology University
Kangwon National University

Research output: Contribution to conference › Conference paper › peer-review

4 Scopus citations

Abstract

Modern information retrieval activities are supported with software systems that facilitate the users' information searching. Information retrieval systems are significantly improved in the past few decades. Now days, there are three types of retrieval models: Boolean, Vector Space and Probabilistic. In this study, we examined the vector space model where documents and queries are represented as vectors. We conducted a number of experiments on the indexing technique of the vector space model to quantitatively describe the effectiveness of the techniques using Lemur Toolkit. The result indicates that stop word removal and steaming techniques improve the quality of the index terms.

Original language	English
Title of host publication	Advances in Computer Science and Ubiquitous Computing - CSA-CUTE2016
Editors	Vincenzo Loia, James J. Jong Hyuk Park, Gangman Yi, Yi Pan
Publisher	Springer Verlag
Pages	680-685
Number of pages	6
ISBN (Print)	9789811030222
DOIs	https://doi.org/10.1007/978-981-10-3023-9_104
State	Published - 2017
Event	11th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2016 - Bangkok, Thailand Duration: 2016.12.19 → 2016.12.21

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	421
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	11th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2016
Country/Territory	Thailand
City	Bangkok
Period	16.12.19 → 16.12.21

Keywords

Indexing
Information retrieval
Similarity function
Vector space model

Quacquarelli Symonds(QS) Subject Topics

Engineering - Mechanical

Access to Document

10.1007/978-981-10-3023-9_104

Cite this

Shin, J. H., Abebe, M., Yoo, C. J., Kim, S., Lee, J. H., & Yoo, H. K. (2017). Evaluating the effectiveness of the vector space retrieval model indexing. In V. Loia, J. J. Jong Hyuk Park, G. Yi, & Y. Pan (Eds.), Advances in Computer Science and Ubiquitous Computing - CSA-CUTE2016 (pp. 680-685). (Lecture Notes in Electrical Engineering; Vol. 421). Springer Verlag. https://doi.org/10.1007/978-981-10-3023-9_104

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title = "Evaluating the effectiveness of the vector space retrieval model indexing",

abstract = "Modern information retrieval activities are supported with software systems that facilitate the users' information searching. Information retrieval systems are significantly improved in the past few decades. Now days, there are three types of retrieval models: Boolean, Vector Space and Probabilistic. In this study, we examined the vector space model where documents and queries are represented as vectors. We conducted a number of experiments on the indexing technique of the vector space model to quantitatively describe the effectiveness of the techniques using Lemur Toolkit. The result indicates that stop word removal and steaming techniques improve the quality of the index terms.",

keywords = "Indexing, Information retrieval, Similarity function, Vector space model",

author = "Shin, \{Jung Hoon\} and Mesfin Abebe and Yoo, \{Cheol Jung\} and Suntae Kim and Lee, \{Jeong Hyu\} and Yoo, \{Hee Kyung\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 11th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2016 ; Conference date: 19-12-2016 Through 21-12-2016",

year = "2017",

doi = "10.1007/978-981-10-3023-9\_104",

language = "English",

isbn = "9789811030222",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "680--685",

editor = "Vincenzo Loia and \{Jong Hyuk Park\}, \{James J.\} and Gangman Yi and Yi Pan",

booktitle = "Advances in Computer Science and Ubiquitous Computing - CSA-CUTE2016",

}

Shin, JH, Abebe, M, Yoo, CJ, Kim, S, Lee, JH & Yoo, HK 2017, Evaluating the effectiveness of the vector space retrieval model indexing. in V Loia, JJ Jong Hyuk Park, G Yi & Y Pan (eds), Advances in Computer Science and Ubiquitous Computing - CSA-CUTE2016. Lecture Notes in Electrical Engineering, vol. 421, Springer Verlag, pp. 680-685, 11th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2016, Bangkok, Thailand, 16.12.19. https://doi.org/10.1007/978-981-10-3023-9_104

Evaluating the effectiveness of the vector space retrieval model indexing. / Shin, Jung Hoon; Abebe, Mesfin; Yoo, Cheol Jung et al.
Advances in Computer Science and Ubiquitous Computing - CSA-CUTE2016. ed. / Vincenzo Loia; James J. Jong Hyuk Park; Gangman Yi; Yi Pan. Springer Verlag, 2017. p. 680-685 (Lecture Notes in Electrical Engineering; Vol. 421).

Research output: Contribution to conference › Conference paper › peer-review

TY - GEN

T1 - Evaluating the effectiveness of the vector space retrieval model indexing

AU - Shin, Jung Hoon

AU - Abebe, Mesfin

AU - Yoo, Cheol Jung

AU - Kim, Suntae

AU - Lee, Jeong Hyu

AU - Yoo, Hee Kyung

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2017.

PY - 2017

Y1 - 2017

N2 - Modern information retrieval activities are supported with software systems that facilitate the users' information searching. Information retrieval systems are significantly improved in the past few decades. Now days, there are three types of retrieval models: Boolean, Vector Space and Probabilistic. In this study, we examined the vector space model where documents and queries are represented as vectors. We conducted a number of experiments on the indexing technique of the vector space model to quantitatively describe the effectiveness of the techniques using Lemur Toolkit. The result indicates that stop word removal and steaming techniques improve the quality of the index terms.

AB - Modern information retrieval activities are supported with software systems that facilitate the users' information searching. Information retrieval systems are significantly improved in the past few decades. Now days, there are three types of retrieval models: Boolean, Vector Space and Probabilistic. In this study, we examined the vector space model where documents and queries are represented as vectors. We conducted a number of experiments on the indexing technique of the vector space model to quantitatively describe the effectiveness of the techniques using Lemur Toolkit. The result indicates that stop word removal and steaming techniques improve the quality of the index terms.

KW - Indexing

KW - Information retrieval

KW - Similarity function

KW - Vector space model

UR - https://www.scopus.com/pages/publications/85007324989

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DO - 10.1007/978-981-10-3023-9_104

M3 - Conference paper

AN - SCOPUS:85007324989

SN - 9789811030222

T3 - Lecture Notes in Electrical Engineering

SP - 680

EP - 685

BT - Advances in Computer Science and Ubiquitous Computing - CSA-CUTE2016

A2 - Loia, Vincenzo

A2 - Jong Hyuk Park, James J.

A2 - Yi, Gangman

A2 - Pan, Yi

PB - Springer Verlag

T2 - 11th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2016

Y2 - 19 December 2016 through 21 December 2016

ER -

Evaluating the effectiveness of the vector space retrieval model indexing

Abstract

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Conference

Keywords

Quacquarelli Symonds(QS) Subject Topics

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