  The term natural language refers to any system of symbolic communication
(spoken, signed or written) without intentional human planning and design. This
distinguishes natural languages such as Arabic and Japanese from artificially
constructed languages such as Esperanto or Python. Natural language processing
(NLP) is the sub-field of artificial intelligence (AI) focused on modeling
natural languages to build applications such as speech recognition and
synthesis, machine translation, optical character recognition (OCR), sentiment
analysis (SA), question answering, dialogue systems, etc. NLP is a highly
interdisciplinary field with connections to computer science, linguistics,
cognitive science, psychology, mathematics and others. Some of the earliest AI
applications were in NLP (e.g., machine translation); and the last decade
(2010-2020) in particular has witnessed an incredible increase in quality,
matched with a rise in public awareness, use, and expectations of what may have
seemed like science fiction in the past. NLP researchers pride themselves on
developing language independent models and tools that can be applied to all
human languages, e.g. machine translation systems can be built for a variety of
languages using the same basic mechanisms and models. However, the reality is
that some languages do get more attention (e.g., English and Chinese) than
others (e.g., Hindi and Swahili). Arabic, the primary language of the Arab
world and the religious language of millions of non-Arab Muslims is somewhere
in the middle of this continuum. Though Arabic NLP has many challenges, it has
seen many successes and developments. Next we discuss Arabic's main challenges
as a necessary background, and we present a brief history of Arabic NLP. We
then survey a number of its research areas, and close with a critical
discussion of the future of Arabic NLP.
