Conference: The 55th Annual Meeting of the Association for Computational Linguistics
Authors: Mohammed Elrazzaz, Shady Elbassuoni, Khaled Shaban, Chadi Helwe
Many unsupervised learning techniques have been proposed to obtain meaningful representations of words from text. In this study, we evaluate these various techniques when used to generate Arabic word embeddings. We first build a benchmark for the Arabic language that can be utilized to perform intrinsic evaluation of different word embeddings. We then perform additional extrinsic evaluations of the embeddings based on two NLP tasks.