NLP is the most effective way to build CAs especially when its techniques try to deal with utterances semantically, which means understanding the utterance’s subject and its content. Understanding a sentence needs to understand the sentence’s structure, its components (words) and the relations among those components. Arabic and English have a linguistic morphology feature. According to a “Morphology is the study of internal word structure”. It is usually focusing on two fundamental issues: firstly, derivational morphology, which concerns how words are formed; and secondly, inflectional morphology, which concerns how words interact with the syntax. However, derivational morphology governs the principles of a word’s transformation from its root. For instance, from”كتب“ ,”the root “يكتب”, many different words might be generated such as “يكتب” “he is writing “she is writing”, “ تكتب” “written or a letter”, and “ مكتوب” “library”. This richness of generating words with different semantic meanings presents a challenge to any kind of Arabic based computational processing. Morphological features work upon integrated dependencies among several linguistic factors, such as vowels, affixes and root-based systems, as the rest of this section will describe.

Based on state-of-the-art deep learning algorithms, our AI team delivers an in-house developed Arabic NLP framework. We leverage state-of-the-art NLP algorithms fine-tuned for the Arabic language to perform downstream tasks with challenging accuracy, such as: sentiment analysis, keyworks identification, topic classification, summarization, intent understanding, named entity recognition etc.  
The power of deep learning is harnessed to deliver this cutting-edge technology that can fit any system. From intelligent witty chatbots, to mining social media content for relevant information to your business and bringing them directly to your dashboard.  
Our deep learning models are up to date with the state-of-the-art deep contextualized dynamic language modelling. This is particularly useful in a language as rich as Arabic where polysemy is and long-term dependencies are common. While Arabic enjoys a variety of significantly different dialects, our NLP frameworks identifies the dialect and the domain of the data (e.g. social media text, or articles).  
Overall, our framework is uniquely advantaged by its interdisciplinary and through capabilities that delivers a coherent and comprehensive story about the data it analyzes. Moreover, our framework has the attractive feature of being “morphable”, as it can be embedded inside any existing system or added in the underlying layer of new ones utilizing a variety of integration schemes.