A morphological word embedding for Arabic language

Abstract

Word embedding is a technique for representing words as dense real vectors and is crucial for most natural language processing tasks. Common approaches include non-contextual embeddings such as Word2vec and contextual embeddings like BERT, both of which operate on the principle that words appearing in similar contexts have close vector representations. Despite their effectiveness, these methods often fail to capture some morphological information useful in many natural language processing applications. In this study, we propose an original word embedding of Arabic words based on the morphological characteristics of the words. Tests show that integrating this morphology-based word embedding into a topic detection model outperforms models using Word2vec or AraBERT embeddings.