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...

In this paper we have presented a brief current state of the Art for Arabic text representation and classification methods. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we invest...