01981nas a2200241   4500008004100000245006500041210006500106260000900171300001400180490000600194520126500200653002401465653002001489653001901509653000801528653002501536653000701561653000901568100001401577700001501591700001601606856011701622       2011                        eng d00aEvaluation of topic identification methods on arabic corpora0 aEvaluation of topic identification methods on arabic corpora  c2011  a185 - 1920 v93 aTopic 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.10aAlwatan-2004 corpus10aArabic language10aNeural network10aSVM10aTopic Identification10aTR10aTULM1 aAbbas, M.1 aSmaili, K.1 aBerkani, D.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84855410016&partnerID=40&md5=8736f4304c67d00c33512bbcb14394fa01675nas a2200169   4500008004100000245004600041210004300087260000900130300001400139490000600153520113900159653002001298653002101318653003401339100001501373856011701388       2010                        eng d00aOn Arabic texts compression and searching0 aArabic texts compression and searching  c2010  a355 - 3610 v83 aWith 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.10aArabic language10aData compression10aSearching in compressed files1 aSallay, H.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960677042&partnerID=40&md5=e14b05b52c375cabf39af4b4801c5c9101449nas a2200205   4500008004100000245007100041210006900112260000900181300001400190490000600204520077700210653002000987653000801007653001901015653003301034653002401067100001501091700002001106856011701126       2010                        eng d00aImproving Arabic text categorization using Neural Network with SVD0 aImproving Arabic text categorization using Neural Network with S  c2010  a233 - 2390 v83 aIn 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.10aArabic language10aMLP10aNeural network10aSingular Value Decomposition10aText categorization1 aHarrag, F.1 aAl-Qawasmah, E.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960685734&partnerID=40&md5=231f569e3eb222816e6af0bb1e4a5cc4