Unsupervised generation of Arabic words

Abstract

Automated word generation might be seen as the reverse process of morphology learning. The aim is to automatically coin valid words in the targeted language. As many other challenges in the field of natural language processing (NLP), the building of the generation engine might be carried out using a supervised or unsupervised approach. The former requires a clean learning data set of a decent size whereas the later needs no more than a plain text. Nonetheless, the unsupervised approaches are usually blamed for their low accuracy. The present article reports the results of an investigation on a context free generation of classical Arabic words. Unsupervised and relatively simple, The proposed approach reached easily an accuracy of 90%.