## 3 Related Work  
Arabic, a widely spoken language with 422 million speakers across 27 countries [16], faces a significant shortage of resources when it comes to building Arabic word embedding models. This scarcity is evident in the limited number of attempts made in this area. Nonetheless, there have been commendable efforts in this regard. Two notable works [9; 11] have made substantial contributions to the field of Arabic word embeddings. Despite the challenges, these works have made significant strides in enhancing our understanding of Arabic language representation in computational models.  
[PERSON] [11] introduced \"AraVec\", an open source project for word embeddings. AraVec aims to provide the Arabic NLP research community with free and powerful word embedding models. The project utilized Twitter, the World Wide Web, and Wikipedia as training resources and developed six different models using CBOW and skip-gram structures. Their quantitative evaluation revealed that the skip-gram model with the Twitter dataset produced the most promising results.  
Additionally, [PERSON] et al. [9] conducted a comparative study of word embeddings, creating 180 different models using Word2 vec, GloVe, and FastText. Their dataset sources included Wikipedia and various social media platforms. In their evaluations, intrinsic and extrinsic assessments were employed. Their findings indicated that Word2 vec models performed well in applications with a semantic nature, such as sentiment analysis, while GloVe outperformed Word2 vec in applications with syntactic and contextual characteristics.  
Table 1 provides a brief comparison between our work and Refs. [9; 11] regarding various aspects such as vector size, window size, Arabic analogy consideration, the availability of models, the availability of corpora, and the usage of a Books corpus.  
As illustrated in Table 1, the work in [11] did not consider various hyperparameter values, a crucial consideration given that the optimal values for Arabic models have not yet been discovered. Furthermore, it did not include analogy testing in the model evaluation. In contrast, while the work in [9] used analogy testing, they utilized a translated version of the Google Analogy Test, which does not provide a fair evaluation. Criticisms have been raised regarding the efficiency of the Google Analogy Test for evaluating non-English word embedding models, particularly for morphologically rich languages such as Arabic and German [10]. The test's depth representation of these languages is insufficient, and it contains a (city-in-state) test section that is culturally irrelevant to languages such as Arabic. Additionally, some vocabulary used in the test may not have exact translations, leading to model failure in producing target results. The availability of both Aravec [11] and other models [9] is also a concern, as they are not publicly accessible.  
To overcome the previously mentioned challenge, we propose building Arabic word embedding models by utilizing existing Arabic books to enrich the corpus. Furthermore, we will experiment with various hyperparameter combinations to construct the models and assess the impact of each combination.  
For our evaluation, we employed an Arabic analogy test derived from [10] and a modified translated version of the Google Analogy Test. By making all the models  
\begin{table}
\begin{tabular}{l|l|l|l|l|l|l} \hline \hline
**Ref.** & **Multiple** & **Multiple** & \multirow{2}{*}{**Arabic Analogy**} & \multirow{2}{*}{**Models Availability**} & \multirow{2}{*}{**Corpus Availability**} & \multirow{2}{*}{**Books Corpus**} \\  & **Vector Size** & & & & \\ \hline AraVec [11] & & & ✓ & & \\ [PERSON] et al. [9] & ✓ & ✓ & & & \\ Our paper & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: A brief comparison between our paper and other Arabic word embeddings.  
and corpora in our work available, we aim to contribute to the enrichment of Arabic NLP and address the current scarcity of resources in the field.