Vol 16, No 2 (2012) > Articles >

Knowledge Dictionary for Information Extraction on the Arabic Text Data

Wahyu Jauharis Saputra 1 , Agus Arifin 1 , Anny Yuniarti 1

Affiliations:

  1. Master Program Department of Informatics, Faculty of Information Technology, ITS Surabaya, Keputih Sukolilo Surabaya 60111, Indonesia

 

Abstract:

Information extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention in many studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledge dictionary is a concept of expression. A knowledge dictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%. 

Keywords: knowledge dictionary, information extraction, data text, Arabic text
Published at: Vol 16, No 2 (2012) pages: 180-184
DOI:

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References:

R.J. Mooney, U.Y. Nahm, Proceedings of the 4th International MIDP Colloquium, September 2003, Bloemfontein, South Africa, W. Daelemans, T. du Plessis, C. Snyman, L. Teck (Eds.), Van Schaik Pub., South Africa, 2005, p.141.

N. Kanya, S. Geetha, IET-UK International Conference on Information and Communication

Technology in Electrical Sciences (ICTES 2007), Dr. M.G.R. University, Chennai, Tamil Nadu,

India, 2007, p.1111.

S. Patwardhan, E. Rillof, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, 2007, p.717.

Y. Ichimura, Y. Nakayama, M. Miyoshi, T. Akahane, T. Sekiguchi, Y. Fujiwara, Proceedings

of the 14th Annual Conference of JSAI, Japan, 2000, p.532.

J.-Z. Hu, T. Xu, J.-B. Shu, P. Lu, 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), Chengdu, China, 2010, p.V4-344. Doi:10.1109/ICACTE.2010.5579485.

J. Zhang, Y. Sun, H. Wang, Y. He, J. Converg. Inf. Technolo. 6/2 (2011) 22.

S. Sakurai, Y. Ichimura, A. Suyama, R. Orihara, IJCAI 2001 Workshop on Text Learning: Beyond Supervision, 2001, p.45.

S. Sakurai, Y. Ichimura, A. Suyama, R. Orihara, ISMIS 2002, LNAI 2366, Springer-Verlag Berling Heidelberg 2002, p.103.

S. Sakurai, A. Suyama, Apll. Soft Comput. 6 (2005) 62.

D. Mona, H. Kadri, J. Daniel, Proceeding HLTNAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers, Stroudsburg, PA, USA, 2004, p.149.

P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley, Boston, 2005, p.500.