Arabic NLP: Unique Challenges and Their Solutions

Pre-processing Arabic text for machine-learning using the camel-tools Python package

image under license to Richard Pelgrim

In this article, I provide a concise and to-the-point overview of the challenges of working with Arabic text in NLP projects…and the tools available to overcome them. I rely heavily on the camel-tools Python package developed at the NYU Abu Dhabi CAMeL Lab and this excellent webinar by its director, Dr. Nizar Habash. Big shout-out to them for doing groundbreaking work in the field and making their tools accessible to the public!

Challenges

Working with Arabic text in NLP projects presents (at least) 5 unique challenges:

1. The form of characters and spelling of words can vary depending on their context (fancy term: Orthographic Ambiguity)
2. The same verb can have thousands (literally) of different forms (fancy term: Morphological Richness)
3. There are many dialects of Arabic and there are big differences between them (Dialectal Variation)
4. Since Arabic is a phonetic language (what you write is what you say), there can be different ways to write the same word when writing in dialectal Arabic, for which there is no agreed-upon standard (Orthographic Inconsistency).