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A Comparative Study of Word Embedding Models for Arabic Text Processing


Fatmah Assiri and Nuha Alghamdi


Vol. 22  No. 8  pp. 399-403


Natural texts are analyzed to obtain their intended meaning to be classified depending on the problem under study. One way to represent words is by generating vectors of real values to encode the meaning; this is called word embedding. Similarities between word representations are measured to identify text class. Word embeddings can be created using word2vec technique. However, recently fastText was implemented to provide better results when it is used with classifiers. In this paper, we will study the performance of well-known classifiers when using both techniques for word embedding with Arabic dataset. We applied them to real data collected from Wikipedia, and we found that both word2vec and fastText had similar accuracy with all used classifiers.


Word Embeddings, Arabic Text Processing, FastText, Word2Vec, NLP.