@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 {135,
	title = {Co-training for search-based automatic image annotation},
	journal = {Journal of Digital Information Management},
	volume = {6},
	year = {2008},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2008},
	pages = {214 - 218},
	abstract = {Search-based automatic image annotation is an effective technology to enhance the performance of annotation. By integrating the co-training technique, this paper addresses on a novel scheme for search-based image annotation, in which two classifiers can contribute to each other during the training phase. Since each classifier can select some most confident images to enhance the generalization ability of the other one, the co-training learning algorithm is triggered out for automatically mining more and more relevant images, which improve the annotation performance greatly. To characterize the various contribution of each relevant image, the probability output of the classification is taken as the corresponding weight. Moreover, the histogram of retrieved keywords is proposed to re-rank the final reliability of the keywords to be annotated, which can also guarantee the scalability property of annotation to some extent. With the promoted search precision on the basis of co-training strategy, the experimental results demonstrate the improvement of annotation performance.},
	keywords = {Automatic image annotation, Bayesian classifier, Co-training, SVM},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-72449186106\&partnerID=40\&md5=73219b4c7b5383b4a06f69f6ba3903d1},
	author = {Zhao, Y. and Zhao, Y. and Zhu, Z.}
}
