Supervised graph-based term weighting scheme for effective text classification

Niloofer Shanavas; Hui Wang; Zhiwei Lin; Glenn Hawe

doi:10.3233/978-1-61499-672-9-1710

Supervised graph-based term weighting scheme for effective text classification

Niloofer Shanavas, Hui Wang, Zhiwei Lin, Glenn Hawe

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

29 Downloads (Pure)

Abstract

Due to the increase in electronic documents, automatic text classification has gained a lot of importance as manual classification of documents is time-consuming. Machine learning is the main approach for automatic text classification, where texts are represented, terms are weighted on the basis of the chosen representation and a classification model is built. Vector space model is the dominant text representation largely due to its simplicity. Graphs are becoming an alternative text representation that have the ability to capture important information in text such as term order, term co-occurrence and term relationships that are not considered by the vector space model. Substantially better text classification performance has been demonstrated for term weighting schemes which use a graph representation. In this paper, we introduce a graph-based term weighting scheme, tw-srw, which is an effective supervised term weighting method that considers the co-occurrence information in text for increasing text classification accuracy. Experimental results show that it outperforms the state-of-the-art unsupervised term weighting schemes.

Original language	English
Title of host publication	Frontiers in Artificial Intelligence and Applications
Editors	Gal A. Kaminka, Frank Dignum, Eyke Hullermeier, Paolo Bouquet, Virginia Dignum, Maria Fox, Frank van Harmelen
Publisher	IOS Press
Pages	1710-1711
Number of pages	2
ISBN (Electronic)	9781614996712
DOIs	https://doi.org/10.3233/978-1-61499-672-9-1710
Publication status	Published - 2016
Externally published	Yes
Event	22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands Duration: 29 Aug 2016 → 02 Sept 2016

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	285
ISSN (Print)	0922-6389

Conference

Conference	22nd European Conference on Artificial Intelligence, ECAI 2016
Country/Territory	Netherlands
City	The Hague
Period	29/08/2016 → 02/09/2016

Bibliographical note

Publisher Copyright:
© 2016 The Authors and IOS Press.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

text classification
term weighting
graph
co-occurrence

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.3233/978-1-61499-672-9-1710Licence: CC BY-NC

Supervised Graph-Based Term Weighting Scheme for Effective Text Classification
© 2016 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)
Final published version, 172 KBLicence: CC BY-NC

Cite this

Shanavas, N., Wang, H., Lin, Z., & Hawe, G. (2016). Supervised graph-based term weighting scheme for effective text classification. In G. A. Kaminka, F. Dignum, E. Hullermeier, P. Bouquet, V. Dignum, M. Fox, & F. van Harmelen (Eds.), Frontiers in Artificial Intelligence and Applications (pp. 1710-1711). (Frontiers in Artificial Intelligence and Applications; Vol. 285). IOS Press. https://doi.org/10.3233/978-1-61499-672-9-1710

Shanavas, Niloofer ; Wang, Hui ; Lin, Zhiwei et al. / Supervised graph-based term weighting scheme for effective text classification. Frontiers in Artificial Intelligence and Applications. editor / Gal A. Kaminka ; Frank Dignum ; Eyke Hullermeier ; Paolo Bouquet ; Virginia Dignum ; Maria Fox ; Frank van Harmelen. IOS Press, 2016. pp. 1710-1711 (Frontiers in Artificial Intelligence and Applications).

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abstract = "Due to the increase in electronic documents, automatic text classification has gained a lot of importance as manual classification of documents is time-consuming. Machine learning is the main approach for automatic text classification, where texts are represented, terms are weighted on the basis of the chosen representation and a classification model is built. Vector space model is the dominant text representation largely due to its simplicity. Graphs are becoming an alternative text representation that have the ability to capture important information in text such as term order, term co-occurrence and term relationships that are not considered by the vector space model. Substantially better text classification performance has been demonstrated for term weighting schemes which use a graph representation. In this paper, we introduce a graph-based term weighting scheme, tw-srw, which is an effective supervised term weighting method that considers the co-occurrence information in text for increasing text classification accuracy. Experimental results show that it outperforms the state-of-the-art unsupervised term weighting schemes.",

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Shanavas, N, Wang, H, Lin, Z & Hawe, G 2016, Supervised graph-based term weighting scheme for effective text classification. in GA Kaminka, F Dignum, E Hullermeier, P Bouquet, V Dignum, M Fox & F van Harmelen (eds), Frontiers in Artificial Intelligence and Applications. Frontiers in Artificial Intelligence and Applications, vol. 285, IOS Press, pp. 1710-1711, 22nd European Conference on Artificial Intelligence, ECAI 2016, The Hague, Netherlands, 29/08/2016. https://doi.org/10.3233/978-1-61499-672-9-1710

Supervised graph-based term weighting scheme for effective text classification. / Shanavas, Niloofer; Wang, Hui; Lin, Zhiwei et al.
Frontiers in Artificial Intelligence and Applications. ed. / Gal A. Kaminka; Frank Dignum; Eyke Hullermeier; Paolo Bouquet; Virginia Dignum; Maria Fox; Frank van Harmelen. IOS Press, 2016. p. 1710-1711 (Frontiers in Artificial Intelligence and Applications; Vol. 285).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Wang, Hui

AU - Lin, Zhiwei

AU - Hawe, Glenn

PY - 2016

Y1 - 2016

N2 - Due to the increase in electronic documents, automatic text classification has gained a lot of importance as manual classification of documents is time-consuming. Machine learning is the main approach for automatic text classification, where texts are represented, terms are weighted on the basis of the chosen representation and a classification model is built. Vector space model is the dominant text representation largely due to its simplicity. Graphs are becoming an alternative text representation that have the ability to capture important information in text such as term order, term co-occurrence and term relationships that are not considered by the vector space model. Substantially better text classification performance has been demonstrated for term weighting schemes which use a graph representation. In this paper, we introduce a graph-based term weighting scheme, tw-srw, which is an effective supervised term weighting method that considers the co-occurrence information in text for increasing text classification accuracy. Experimental results show that it outperforms the state-of-the-art unsupervised term weighting schemes.

AB - Due to the increase in electronic documents, automatic text classification has gained a lot of importance as manual classification of documents is time-consuming. Machine learning is the main approach for automatic text classification, where texts are represented, terms are weighted on the basis of the chosen representation and a classification model is built. Vector space model is the dominant text representation largely due to its simplicity. Graphs are becoming an alternative text representation that have the ability to capture important information in text such as term order, term co-occurrence and term relationships that are not considered by the vector space model. Substantially better text classification performance has been demonstrated for term weighting schemes which use a graph representation. In this paper, we introduce a graph-based term weighting scheme, tw-srw, which is an effective supervised term weighting method that considers the co-occurrence information in text for increasing text classification accuracy. Experimental results show that it outperforms the state-of-the-art unsupervised term weighting schemes.

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BT - Frontiers in Artificial Intelligence and Applications

A2 - Kaminka, Gal A.

A2 - Dignum, Frank

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A2 - Bouquet, Paolo

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PB - IOS Press

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Y2 - 29 August 2016 through 2 September 2016

ER -

Shanavas N, Wang H, Lin Z, Hawe G. Supervised graph-based term weighting scheme for effective text classification. In Kaminka GA, Dignum F, Hullermeier E, Bouquet P, Dignum V, Fox M, van Harmelen F, editors, Frontiers in Artificial Intelligence and Applications. IOS Press. 2016. p. 1710-1711. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/978-1-61499-672-9-1710

Supervised graph-based term weighting scheme for effective text classification

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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