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Text pre-processing of Arabic Language is a challenge and crucial stage in Text Categorization (TC) particularly and Text Mining (TM) generally. Stemming algorithms can be employed in Arabic text pre-processing to reduces words to their stems/or root. Arabic stemming algorithms can be ranked, according to three category, as root-based approach (ex. Khoja);(More)
Text preprocessing of Arabic Language is a challenge and crucial stage in Text Categorization (TC) particularly and Text Mining (TM) generally. Stemming algorithms can be used in Arabic text preprocessing to reduce multiple forms of the word to one form (root or stem). Arabic stemming algorithms can be classified, according to the desired level of analysis,(More)
One of the major problems of modern Information Retrieval (IR) systems is the vocabulary problem that concerns the discrepancies between terms used for describing documents and the terms used by the researchers to describe their information need. In this paper, we propose to use the well known abstractive model -Latent Semantic Analysis (LSA)- with a wide(More)
Representation of semantic information contained in the words is needed for any Arabic Text Mining applications. More precisely, the purpose is to better take into account the semantic dependencies between words expressed by the co-occurrence frequencies of these words. There have been many proposals to compute similarities between words based on their(More)
Part of speech tagging (POS tagging) has a crucial role in different fields of natural language processing (NLP) including Speech Recognition, Natural Language Parsing, Information Retrieval and Multi Words Term Extraction. This paper proposes an efficient and accurate POS Tagging technique for Arabic language using hybrid approach. Due to the ambiguity(More)
Document Clustering algorithms goal is to create clusters that are coherent internally, but clearly different from each other. The useful expressions in the documents is often accompanied by a large amount of noise that is caused by the use of unnecessary words, so it is indispensable to eliminate it and keeping just the useful information. Keyphrases(More)
Document Clustering is a branch of a larger area of scientific study known as data mining .which is an unsupervised classification using to find a structure in a collection of unlabeled data. The useful information in the documents can be accompanied by a large amount of noise words when using Full Text Representation, and therefore will affect negatively(More)
Part-of-speech (POS) tagger plays an important role in Natural Language Applications like Speech Recognition, Natural Language Parsing, Information Retrieval and Multi Words Term Extraction. This study proposes a building of an efficient and accurate POS Tagging technique for Arabic language using statistical approach. Arabic Rule-Based method suffers from(More)