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Classification of Hadiths using LVQ based on VSM Considering Words Order

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Mohamed Ghanem, Abdelaaziz Mouloudi, Mohammed Mourchid
10.5120/ijca2016911077

Mohamed Ghanem, Abdelaaziz Mouloudi and Mohammed Mourchid. Classification of Hadiths using LVQ based on VSM Considering Words Order. International Journal of Computer Applications 148(4):25-28, August 2016. BibTeX

@article{10.5120/ijca2016911077,
	author = {Mohamed Ghanem and Abdelaaziz Mouloudi and Mohammed Mourchid},
	title = {Classification of Hadiths using LVQ based on VSM Considering Words Order},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2016},
	volume = {148},
	number = {4},
	month = {Aug},
	year = {2016},
	issn = {0975-8887},
	pages = {25-28},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume148/number4/25746-2016911077},
	doi = {10.5120/ijca2016911077},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The religion of Islam is based on a sacred text called Qur’an, a divine speech expressed in Arabic language. Qur’an constitutes the main root of Islam jurisprudence which has a second source of inspiration known as Hadiths. As the Muslim’s life is governed by those holy texts, need of their authenticity is required. Using VSM (Vector Space Model), we can represent Hadiths as a vector of words. The Term Weighting obtained by multiplying term frequency by the inverse document frequency does not take into account the word order, however, order of narrators is critical to classify Hadith. In this paper we propose a new method considering the words order (in our case the narrator’s order), to classify Hadiths into four categories: Sahih, Hasan, Da’if and Maudu’. We use in this purpose LVQ (Learning Vector Quantization). We got good results for classifying Sahih and Maudu’ categories.

References

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Keywords

Arabic Natural Language Processing, Learning vector quantization, Term Weighting, Text categorization, Vector Space Model.