Document features selection using background knowledge and word clustering technique

Abstract: By everyday development of storage and communicational and electronic media, there are significant amount of information being collected and stored in different forms such as electronic documents and document databases makes it difficult to process them, properly. To extract knowledge from this large volume of documental data, we require the use of documents organizing and indexing methods. Among these methods, we can consider clustering and classification methods where the objective is to organize documents and to increase the speed of accessing to required information. In most of document clustering methods, the clustering is mostly executed based on word frequency and considering document as a bag of words. In this essay, in order to decrease the number of features and to choose basic document feature, we use background knowledge and word clustering methods. In fact by using WordNet ontology, background knowledge and clustering method, the similar words of documents are clustered and the clusters with the number of words more than threshold are chosen and then their frequency of words is accepted as the effective features of document. The results of this proposed method simulation shows that the documents dimensions are decreased effectively and consequently the performance of documents clustering is increased.

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 Farahmand, H., Harounabadi, A & Mirabedini, S. (2014). Document features selection using background knowledge and word clustering technique.Management Science Letters , 4(2), 241-250.

Refrences

Bloehdorn, S., Cimiano, P., Hotho, A., & Staab, S. (2005, May). An Ontology-based Framework for Text Mining. In LDV Forum, 20(1), 87-112.

Bracewell, D., Ren, F., & Kuroiwa, S. (2005). Multilingual Single Document Keyword Extraction for Information Retrieval. In Proc. IEEE Int. Conf. Natural Language Processing and Knowledge Eng, 517-522.

Breaux, T. D., & Reed, J. W. (2005). Using Ontology in Hierarchical Information Clustering. In Proc. 38th Hawaii Int. Conf. System Sciences.

Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent data analysis, 1(3), 131-156.

Fellbaum, C. (1998). WordNet: an electronic lexical database. MIT Press.

Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research, 3, 1289-1305.

Guyon, I., & Elisseff, A. (2003). An Introduction to variable and feature selection. Machine Learning Research 3, 1157-1182.

Han, H., Manavoglu, E., Zha, H., Tsioutsiouliklis, K., Lee Giles, C., & Zhang, X. (2005). Rule-based Word Clustering for Document Metadata Extraction. ACM Symp. On Applied Computing, 1049-1053).

Hotho, A., Staab, S., & Stumme, G. (2003). Ontologies Improve Text Clustering. In Proc. ICDM’03 3rd IEEE Int. Conf. on Data Mining (pp. 541).

Kohavi, R., & Sommerfield, D. (1995). Feature subset selection using wrapper methods: verfitting and dynamic search space topology. In Proc. 1st International Conference on Knowledge Discovery and Data Mining (pp. 192-197).

Kononenko, I. (1994). Estimating attributes: analysis and extension of RELLIEF. In Proc. 6th European Conference on Machine Learning (ECML-94) (pp. 171-182).

Lewis, D.D., Yang, Y., Rose, T., & Li, F. (2004). RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5, 361-397).

Makrehchi, M., & Kamel, M. (2004, June). A fuzzy set approach to extracting keywords from abstracts. In Fuzzy Information, 2004. Processing NAFIPS & apos; 04. IEEE Annual Meeting of the (Vol. 2, pp. 528-532). IEEE.

Schone, P., & Jurafsky, D. (2001). Knowledge-free induction of inflectional morphologies. In Proc. North American Chapter of the Association for Computational Linguistics on Language technologies (pp. 1-9).

Sebastiani, F. (2002). Machine learning automated text categorization. ACM Computing Surveys, 34(1), 1–47.

Shang, W., Huang, H., Zhu, H., Lin, Y., Qu, Y., & Wang, Z. (2007). A novel feature selection algorithm for text categorization. Expert Systems with Applications, 33(1), 1–5.

Yang, Y., & Pedersen, J.P. (1995). A comparative study on feature selection in text categorization. 14th Int. Conf. Machine Learning (pp. 412–420).

Zheng, Z., & Srihari, R. (2003). Optimally combining positive and negative features for text categorization. ICML Workshop.

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Management Science Letters

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