How to Use Arabic Word2Vec Word Embedding with LSTM

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Word embedding is the approach of learning word and their relative meanings from a corpus of text and representing the word as a dense vector. The word vector is the projection of the word into a continuous feature vector space, see Figure 1 (A) for clarity. Words that have similar meaning should be close together in the vector space as illustrated in see Figure 1 (B). Word2vec is one of the most popular words embedding in NLP. Word2vec has two types, Continuous Bag-of-Words Model (CBOW) and Continuous Skip-gram Model [3], the model architectures are shown in Figure 2. CBOW predicts the word according to the given context, where Skip-gram predicts the context according to the given word, which increases the computational complexity [3].

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