Improving Persian Text Classification and Clustering Using Persian Thesaurus

This paper proposes an innovative approach to improve the classification performance of Persian texts. The proposed method uses a thesaurus as a helpful knowledge to obtain more representative word-frequencies in the corpus. Two types of word relationships are considered in our used thesaurus. This is the first attempt to use a Persian thesaurus in the field of Persian information retrieval. Experimental results indicate the performance of text classification improves significantly in the case of employing Persian thesaurus rather the case of ignoring Persian thesaurus.

[1]  Alex Alves Freitas,et al.  Automatic Text Summarization Using a Machine Learning Approach , 2002, SBIA.

[2]  Hamid Parvin,et al.  Improving Persian Text Classification Using Persian Thesaurus , 2011, CIARP.

[3]  Elena Demidova Automatic Keyword Extraction , 2009 .

[4]  Shamkant B. Navathe,et al.  Comparison of two schemes for automatic keyword extraction from MEDLINE for functional gene clustering , 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

[5]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[6]  Michel C. A. Klein,et al.  Thesaurus-based Retrieval of Case Law , 2006, JURIX.

[7]  Yoseop Woo,et al.  Automated Keyword Extraction Using Category Correlation of Data , 2006, ICCSA.

[8]  Anette Hulth,et al.  Improved Automatic Keyword Extraction Given More Linguistic Knowledge , 2003, EMNLP.

[9]  Ian H. Witten,et al.  Thesaurus based automatic keyphrase indexing , 2006, Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '06).

[10]  Evangelos E. Milios,et al.  World Wide Web site summarization , 2004, Web Intell. Agent Syst..

[11]  Peter D. Turney Learning Algorithms for Keyphrase Extraction , 2000, Information Retrieval.

[12]  José Luis Martínez-Fernández,et al.  Automatic Keyword Extraction for News Finder , 2003, Adaptive Multimedia Retrieval.

[13]  Hideki Mima,et al.  Automatic recognition of multi-word terms:. the C-value/NC-value method , 2000, International Journal on Digital Libraries.