Arabic Text categorization is considered one of the severe problems in classification using machine learning algorithms. Achieving high accuracy in Arabic text categorization depends on the preprocessing techniques used to prepare the data set. Thus, in this paper, an investigation of the impact of the preprocessing methods concerning the performance of three machine learning algorithms, namely...

Text mining techniques confront many challenges when dealing with the Arabic language including lexical disambiguation because Arabic is a highly inflectional and derivational language, most of the Arabic texts are devoid of diacritics especially Modern Standard Arabic (MSA), thus, it is a must to depend on the ambiguous word context under study. Two fuzzy logic classifiers have been built and ...

Computational Morphology is an urgent problem for Arabic Natural Language Processing, because Arabic is a highly inflected language. We have found, however, that a full solution to this problem is not required for effective information retrieval. Light stemming allows remarkably good information retrieval without providing correct morphological analyses. We developed several light stemmers for ...

This chapter presents an adaptation of existing techniques in Arabic morphology by leveraging corpus statistics to make them suitable for Information Retrieval (IR). The adaptation resulted in the development of Sebawai, an shallow Arabic morphological analyzer, and Al-Stem, an Arabic light stemmer. Both were used to produce Arabic index terms for Arabic experimentation. Sebawai is concerned wi...

The present paper describes the experience gained at LDC in the collection and transcription of conversational dialectal Arabic. The paper will cover the following: (a) Arabic language background; (b) objectives. principles, and methodological choices of dialectal Arabic transcription, (c) design features of LDC‟s „Arabic MultiDialectal Transcription Tool‟ (AMADAT) and metalanguage transcriptio...

This paper deals with building linguistic resources for Gulf Arabic, one of the Arabic variations, for sentiment analysis task using machine learning. To our knowledge, no previous works were done for Gulf Arabic sentiment analysis despite the fact that it is present in different online platforms. Hence, the first challenge is the absence of annotated data and sentiment lexicons. To fill this g...

This paper explains for the Arabic language, how to extract named entities and topics from news articles. Due to the lack of high quality tools for Named Entity Recognition (NER) and topic identification for Arabic, we have built an Arabic NER (RenA) and an Arabic topic extraction tool using the popular LDA algorithm (ALDA). NER involves extracting information and identifying types, such as nam...

This study compared the spatial representation of numbers in three groups of adults: Canadians, who read both English words and Arabic numbers from left to right; Palestinians, who read Arabic words and Arabic-Indic numbers from right to left; and Israelis, who read Hebrew words from right to left but Arabic numbers from left to right. Canadians associated small numbers with left and large numb...

In spite of its robust syntax, semantic cohesion, and less ambiguity, lemma level analysis and generation does not yet focused in Arabic NLP literatures. In the current research, we propose the first non-statistical accurate Arabic lemmatizer algorithm that is suitable for information retrieval (IR) systems. The proposed lemmatizer makes use of different Arabic language knowledge resources to g...