Arabic language is the most spoken languages in the Semitic languages group, and one of the most common languages in the world spoken by more than 422 million. It is also of paramount importance to Muslims, it is a sacred language of the Islamic Holly Book (Quran) and prayer (and other acts of worship) in Islam is performed only by mastering some of Arabic words. Arabic is also a major ritual l...

In general, word stemming is one of the most important factors that affect the performance of information retrieval systems. The optimization issues of Arabic light stemming algorithm as a main component in natural language processing and information retrieval for Arabic language are based on root-pattern schemes. Since Arabic language is a highly inflected language and has a complex morphologi...

In this module, Learning Vector Quantization LVQ neural network is first time introduced as a classifier for Arabic handwriting. Classification has been performed in two different strategies, in first strategy, we use one classifier for all 53 Arabic Character Basic Shapes CBSs in training and testing phases, in second strategy we use three classifiers and three subsets of 53 Arabic CBSs, the t...

Stemming is an essential processing step in a wide range of high level text processing applications such as information extraction, machine translation and sentiment analysis. It is used to reduce words to their stems. Many stemming algorithms have been developed for Modern Standard Arabic (MSA). Although Arabic tweets and MSA are closely related and share many characteristics, there are substa...

This paper reports on the application of the Text Attribution Tool (TAT) to profiling the authors of Arabic emails. The TAT system has been developed for the purpose of language-independent author profiling and has now been trained on two email corpora, English and Arabic. We describe the overall TAT system and the Machine Learning experiments resulting in classifiers for the different author t...

Statistical machine translation for dialectal Arabic is characterized by a lack of data since data acquisition involves the transcription and translation of spoken language. In this study we develop techniques for extracting parallel data for one particular dialect of Arabic (Iraqi Arabic) from out-ofdomain corpora in different dialects of Arabic or in Modern Standard Arabic. We compare two dif...

Clitics in Arabic language can be attached to a stem or to each other without orthographic marks such as an apostrophe. In this paper we present a statistical study of clitics and its effect in Arabic language. We tokenize large Arabic text using white-spaces and an automatic clitics tokenizer (AMIRA 2.0) and compare the unique-word count in both cases with English language. We also show the re...

In this paper, we present the details of creating a pilot Arabic proposition bank (Propbank). Propbanks exist for both English and Chinese. However the morphological and syntactic expression of linguistic phenomena in Arabic yield a very different type of process in creating an Arabic propbank. Hence, we highlight those characteristics of Arabic that make creating a propbank for the language a ...

We demonstrate a data collection and analysis system that can be used to analyze the relative contributions of dialect dependent variation in the lexical of speech-like Arabic text. We utilize Latent Dirichlet Allocation (LDA), a generative Probabilistic modeling method, to analyze a phonetic Latin Spelled Arabic online chat corpus. The corpus produces different word choices and word relations ...

This paper reports the results of the first phase of a research work for building a high performance, speakerindependent natural Arabic speech recognition system. This work aims at developing an Arabic broadcast news transcription system and a base system for further research. Several concurrent recent advances in Arabic language processing were crucial for the success of this stage, e.g automa...