Wael Etaiwi and Arafat Awajan. 2018. Graph-based Arabic NLP Techniques: A Survey. Procedia Computer Science 142, (January 2018), 328–333. DOI: https://doi.org/10.1016/j.procs.2018.10.488


The improvements of natural language processing applications such as machine translation, text summarization and the likes are crucial, and can be achieved using many different techniques including graph, deep learning, word embedding and others. This survey investigates several research studies that have been conducted in the field of Arabic natural language processing using graph representation. The related literature in the use of graph in Arabic Natural Language Processing is limited and relatively new compared to the available literature on other languages, such as English. This paper summarizes the major techniques used in Graph-based Arabic NLP techniques, and discusses the role of using graph based techniques to solve natural language processing problems.