@article {287,
	title = {Evaluation of topic identification methods on arabic corpora},
	journal = {Journal of Digital Information Management},
	volume = {9},
	year = {2011},
	note = {Cited By (since 1996):4Export Date: 10 July 2014},
	month = {2011},
	pages = {185 - 192},
	abstract = {Topic Identification is one of the important keys for the success of many applications. Indeed, there are few works in this field concerning Arabic language because of lack of standard corpora. In this study, we will provide directly comparable results of six text categorization methods on a new Arabic corpus Alwatan-2004. Hence, Topic Unigram Language Model (TULM), Term Frequency/Inverse Document Frequency (TFIDF), Neural Network, SVM, M-SVM and TR have been experimented, and showed that TR-Classifier is the most efficient among the set of classifiers, nevertheless, only binary SVM outperformed it thanks to its characteristics. Moreover, we should note that the size of Alwatan-2004 corpus used to achieve our experiments is considered the most important compared to any other Arabic corpus which had been used for topic identification experiments until now. In addition, we aim through using small sizes of vocabularies to reduce the time of computation. This is important for adaptive language modeling, particularly Topic Adaptation, which is required in real time applications such as speech recognition and machine translation systems. Our experiments indicate that the results are better than other works dealing with Arabic text categorization.},
	keywords = {Alwatan-2004 corpus, Arabic language, Neural network, SVM, Topic Identification, TR, TULM},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84855410016\&partnerID=40\&md5=8736f4304c67d00c33512bbcb14394fa},
	author = {Abbas, M. and Smaili, K. and Berkani, D.}
}
@article {252,
	title = {On Arabic texts compression and searching},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Export Date: 10 July 2014},
	month = {2010},
	pages = {355 - 361},
	abstract = {With the dramatic increasing of electronic Arabic content, the text compression techniques will play a major role in several domains and applications such as search engines, data archiving, searching and retrieval from huge databases. Mainly the combination of compression and indexing techniques allows the interesting possibility to work directly on the compressed textual files or databases, which results saving time and resources. The existing compression techniques and tools are generic and do not consider the specific characteristics of the Arabic language such as its derivative nature. Mainly compression techniques should be based on the morphology characteristics of the Arabic language, its grammatical characteristics, the texts subject, and their statistical characteristics. The paper surveys the state of the art of the Arabic texts compression techniques and tools and identifies some research tracks that should be explored in future. It presents also some dedicated Arabic text compression algorithms which save more physical space and speed up the data retrieval text files by searching in their compressed form.},
	keywords = {Arabic language, Data compression, Searching in compressed files},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960677042\&partnerID=40\&md5=e14b05b52c375cabf39af4b4801c5c91},
	author = {Sallay, H.}
}
@article {239,
	title = {Improving Arabic text categorization using Neural Network with SVD},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):3Export Date: 10 July 2014},
	month = {2010},
	pages = {233 - 239},
	abstract = {In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifiable and faster for the convergence of the training process used in the NN model. To test the effectiveness of the proposed model, experiments were conducted using an in-house collected Arabic corpus for text categorization. The results showed that the proposed model was able to achieve high categorization effectiveness as measured by precision, recall and F-measure. Experimental result shows that the ANN model using SVD is better than the basic ANN on Arabic text classification.},
	keywords = {Arabic language, MLP, Neural network, Singular Value Decomposition, Text categorization},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960685734\&partnerID=40\&md5=231f569e3eb222816e6af0bb1e4a5cc4},
	author = {Harrag, F. and Al-Qawasmah, E.}
}
