ABSTRACT
Tokenization is a fundamental step in processing textual data preceding the tasks of information retrieval, text mining, and natural language processing. Tokenization is a language-dependent approach, including normalization, stop words removal, lemmatization and stemming.
Both stemming and lemmatization share a common goal of reducing a word to its base. However, lemmatization is more robust than stemming as it often involves usage of vocabulary and morphological analysis, as opposed to simply removing the suffix of the word. In this work, we introduce a novel lemmatization algorithm for the Arabic Language.
The new lemmatizer proposed here is a part of a comprehensive Arabic tokenization system, with a stop words list exceeding 2200 Arabic words. Currently, there are two Arabic leading stemmers: the root-based stemmer and the light stemmer. We hypothesize that lemmatization would be more effective than stemming in mining Arabic text. We investigate the impact of our new lemmatizer on unsupervised data mining techniques in comparison to the leading Arabic stemmers. We conclude that lemmatization is a better word normalization method than stemming for Arabic text.
- W. B. Frakes, "Stemming algorithms," 1992.Google Scholar
- I. A. Al-Kharashi, "Micro-AIRS: A microcomputer-based Arabic information retrieval system comparing words, stems, and roots as index terms," 1991.Google Scholar
- I. A. Al-Kharashi and M. W. Evens, "Comparing Words, Stems, and Roots as Index Terms in an Arabic Information Retrieval System.," Journal of the American Society for Information Science, vol. 45, 1994, pp. 548--60. Google ScholarDigital Library
- L. S. Larkey and M. E. Connell, "Arabic Information Retrieval at UMass in TREC-10," Proceedings of the Tenth Text REtrieval Conference (TREC-10)", EM Voorhees and DK Harman ed, 2001, pp. 562--570.Google Scholar
- L. S. Larkey, L. Ballesteros, and M. E. Connell, "Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis," Tampere, Finland: ACM, 2002, pp. 275--282. Google ScholarDigital Library
- J. Xu, A. Fraser, and R. Weischedel, "Empirical studies in strategies for Arabic retrieval," Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002, pp. 269--274. Google ScholarDigital Library
- S. Khoja and R. Garside, "Stemming Arabic Text," Lancaster, UK, Computing Department, Lancaster University, 1999.Google Scholar
- R. Duwairi, "A Distance-based Classifier for Arabic Text Categorization," Proceedings of the 2005 International Conference on Data Mining, Las Vegas USA, 2005.Google Scholar
- M. El Kourdi, A. Bensaid, and T. Rachidi, "Automatic Arabic Document Categorization Based on the Naïve Bayes Algorithm," COLING 2004. Google ScholarDigital Library
- S. H. Mustafa and Q. A. Al-Radaideh, "Using N-grams for Arabic text searching," Journal of the American Society for Information Science and Technology, vol. 55, 2004, pp. 1002--1007. Google ScholarDigital Library
- R. A. Baeza-Yates, "Text-Retrieval: Theory and Practice," North-Holland Publishing Co., 1992, pp. 465--476. Google ScholarDigital Library
- "Snowball: A language for stemming algorithms"; http://snowball.tartarus.org/texts/introduction.html.Google Scholar
- S. S. Al-Fedaghi and F. Al-Anzi, "A New Algorithm to Generate Arabic Root-Pattern Forms," Proceedings of the 11th National Computer Conference and Exhibition, 1989, pp. 391--400.Google Scholar
- T. Korenius et al., "Stemming and lemmatization in the clustering of finnish text documents," Washington, D.C., USA: ACM, 2004, pp. 625--633. Google ScholarDigital Library
- M. BOOT, "Homography and Lemmatization in Dutch Texts," ALLC Bulletin, vol. 8, 1980, pp. 175--189.Google Scholar
- Eiman Al-Shammari and J. Lin, "Automated Corpora Creation Using A novel Arabic Stemming Algorithm," The 2008 International Symposium on Using Corpora in Contrastive and Translation Studies (UCCTS), Hangzhou, China: 2008.Google Scholar
- A. K. Jain and R. C. Dubes, Algorithms for clustering data, 1988. Google ScholarDigital Library
- M. Steinbach, G. Karypis, and V. Kumar, "A comparison of document clustering techniques," KDD Workshop on Text Mining, vol. 34, 2000, p. 35.Google Scholar
- Y. Zhao and G. Karypis, "Criterion Functions for Document Clustering," Experiments and Analysis University of Minnesota, Department of Computer Science/Army HPC Research Center.Google Scholar
- E. Al-Shammari, "Towards an Error Free Stemming," IADIS European Conference on Data Mining (ECDM 2008), Amsterdam, The Netherlands: 2008.Google Scholar
Index Terms
- A novel Arabic lemmatization algorithm
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