JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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Az Azrinudin Alidin and Fabio Crestani Schmidt, A., Aidoo, K., Takaluoma, A., Tuomela, U., Van Laerhoven, K., & Van de Velde, W. (1999). Advanced interaction in context. In H.-W. Gellersen (Ed.), Handheld and Ubiquitous Computing (pp. 89–101). Springer Berlin/ Heidelberg. Schmidt, A., Beigl, M., & Gellersen, H. (1999). There is more to context than location. Computers & Graphics, 23(6), 893 – 901. Sohn, T., Li, K. A., Griswold, W. G., & Hollan, J. D. (2008). A diary study of mobile information needs. Paper presented at the twenty-sixth annual SIGCHI conference on Human Factors in Computing Systems, CHI’08, 433–442. Tamine-Lechani, L., Boughanem, M., & Daoud, M. (2010). Evaluation of contextual information retrieval effectiveness: overview of issues and research. Knowledge and Information Systems, 24(1), 1–34. 238 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
ISSN: 0128-7680 © 2013 Universiti Putra Malaysia Press. Pertanika J. Sci. & Technol. 21 (1): 239 - 246 (2013) SCIENCE & TECHNOLOGY Journal homepage: http://www.pertanika.upm.edu.my/ Using SVMs for Classification of Cross-Document Relationships Yogan Jaya Kumar 1,2 *, Naomie Salim 2 , Ahmed Hamza Osman 2 and Albaraa Abuobieda 2 1 Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia 2 Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia ABSTRACT Cross-document Structure Theory (CST) has recently been proposed to facilitate tasks related to multi- document analysis. Classifying and identifying the CST relationships between sentences across topically related documents have since been proven as necessary. However, there have not been sufficient studies presented in literature to automatically identify these CST relationships. In this study, a supervised machine learning technique, i.e. Support Vector Machines (SVMs), was applied to identify four types of CST relationships, namely “Identity”, “Overlap”, “Subsumption”, and “Description” on the datasets obtained from CSTBank corpus. The performance of the SVMs classification was measured using Precision, Recall and F-measure. In addition, the results obtained using SVMs were also compared with those from the previous literature using boosting classification algorithm. It was found that SVMs yielded better results in classifying the four CST relationships. Keywords: CST relation, multi-document, rhetorical relation, SVMs INTRODUCTION Discourse analysis in texts has nowadays become very prominent, especially when it involves multiple texts such as documents. The idea of cross-document structural relationship is to investigate the existence of inter-document rhetorical relationships. These rhetorical relations are based on the CST model (Cross-document Structure Theory) (Radev, 2000). Documents which Article history: Received: 31 March 2012 Accepted: 31 August 2012 E-mail addresses: yogan@utem.edu.my (Yogan Jaya Kumar), naomie@utm.my (Naomie Salim), ahmedagraa@hotmail.com (Ahmed Hamza Osman), albarraa@hotmail.com (Albaraa Abuobieda) *Corresponding Author are related to the same topic usually contain semantically related textual units. These textual units can be words, phrases, sentences, or the documents itself. The general schema of CST is shown in Fig.1. In the current work, only the semantic relations between sentences were taken into consideration. Some examples of such semantic connections are “Identity”,
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Journal of Science & Technology Jou
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International Advisory Board Adarsh
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Selected Articles from UPM-Malaysia
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ISSN: 0128-7680 © 2013 Universiti
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Aspect of Fatigue Analysis of Compo
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Application of Anthropometric Dimen
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Different Media Formulation on Bioc
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CONCLUSION Heng, S. C., Ibrahim, Z.
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Y. Yusuf, J. M. Juoi, Z. M. Rosli,
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Surface Roughness Y. Yusuf, J. M. J
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DISCUSSION Y. Yusuf, J. M. Juoi, Z.
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Modelling of Motion Resistance Rati
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The Empirical Method Modelling of M
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Assessment of Heavy Metal Pollution
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TABLE 5: Heavy metal concentrations
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Physico-Chemical and Electrical Pro
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TABLE 1: (continued) Physico-Chemic
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Norhashila Hashim et al. effectiven
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Statistical Analysis Norhashila Has
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Norhashila Hashim et al. Post-hoc a
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Norhashila Hashim et al. Cubero, S.
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S. Ismail and K. A. Mohd Atan 4 4 p
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S. Ismail and K. A. Mohd Atan The f
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Lemma 1.1 The equation S. Ismail an
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CONCLUSION S. Ismail and K. A. Mohd
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INTRODUCTION Tajau, R. et al. A hig
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Tajau, R. et al. the acrylate’s c
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Tajau, R. et al. double bond) and C
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Aji, I. S., Zinudin, E. S., Khairul
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Aji, I. S., Zinudin, E. S., Khairul
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CONCLUSION Aji, I. S., Zinudin, E.
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REFERENCES N. Maizatul, I. Norazowa
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CBIR Using Colour and Shape Fused F
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Toward Automatic Semantic Annotatin
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Issues on Trust Management in Wirel
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