Language in the Arab world presents a complex diglossic and multilingual setting, involving the use of Modern Standard Arabic, various dialects and sub-dialects, as well as multiple European languages. This diverse linguistic landscape has given rise to code-switching, both within Arabic varieties and between Arabic and foreign languages. The widespread occurrence of code-switching across the region makes it vital to address these linguistic needs when developing language technologies. In this paper, we provide a review of the current literature in the field of code-switched Arabic NLP, offering a broad perspective on ongoing efforts, challenges, research gaps, and recommendations for future research directions.

The introduction of word embedding models has remarkably changed many Natural Language Processing tasks. Word embeddings can automatically capture the semantics of words and other hidden features. Nonetheless, the Arabic language is highly complex, which results in the loss of important information. This paper uses Madamira, an external knowledge source, to generate additional word features. We evaluate the utility of adding these features to conventional word and character embeddings to perform the Named Entity Recognition (NER) task on Modern Standard Arabic (MSA). Our NER model is implemented using Bidirectional Long Short Term Memory and Conditional Random Fields (BiLSTM-CRF). We add morphological and syntactical features to different word embeddings to train the model. The added features improve the performance by different values depending on the used embedding model. The best performance is achieved by using Bert embeddings. Moreover, our best model outperforms the previous systems to the best of our knowledge.

Infographics is a tool for data visualization. It makes data easy to understand and interpret. An infographic is defined as a visual representation for data like a chart or a diagram. Infographics can help in many fields including education. This is due to the fact that information can be easily memorized and understood if was given in a visual form. Infographics are found everywhere and used in many different fields. Facebook timeline is considered as an infographic. On another hand, online journalism is increasingly gaining popularity. It is also considered as a source of big data that is rapidly expanding. Online newspapers and magazines provide a large population with daily important information. The aim of the work is to use data visualization, infographics and Natural Language Processing (NLP) techniques in online journalism. The aim of the work is to automatically visualize the information in an article in the form of infographics.