A novel dataset for quranic words identification and authentication

Quran is the holy book for Muslims around the world. For the past fourteen centuries after its revelation, ithas been preserved in all possible ways from any distortions. The huge increase in Internet usage and the spread of digital media lead to the development of many websites, services, and applications related to Quran. These efforts include the conversion of Quranic verses, translations, explanations,tafseer and other Quranic sciences into digital formats. Some of these efforts are foundless authentic. The authentication dependson correct identification of Quranic words in the text. In this paper, we introduce a novel dataset for Quranic words identification and authentication. The proposed dataset contains more than 93,000 samples with64 features for each extracted in numerical form.The validation tests of the proposed dataset resulted high accuracy average

Arabic nested noun compound extraction based on linguistic features and statistical measures

The extraction of Arabic nested noun compound is significant for several research areas such as sentiment analysis, text summarization, word categorization, grammar checker, and machine translation. Much research has studied the extraction of Arabic noun compound using linguistic approaches, statistical methods, or a hybrid of both. A wide range of the existing approaches concentrate on the extraction of the bi-gram or tri-gram noun compound. Nonetheless, extracting a 4-gram or 5-gram nested noun compound is a challenging task due to the morphological, orthographic, syntactic and semantic variations. Many features have an important effect on the efficiency of extracting a noun compound such as unit-hood, contextual information, and term-hood. Hence, there is a need to improve the effectiveness of the Arabic nested noun compound extraction. Thus, this paper proposes a hybrid linguistic approach and a statistical method with a view to enhance the extraction of the Arabic nested noun compound. A number of pre-processing phases are presented, including transformation, tokenization, and normalisation. The linguistic approaches that have been used in this study consist of a part-of-speech tagging and the named entities pattern, whereas the proposed statistical methods that have been used in this study consist of the NC-value, NTC-value, NLC-value, and the combination of these association measures. The proposed methods have demonstrated that the combined association measures have outperformed the NLC-value, NTC-value, and NC-value in terms of nested noun compound extraction by achieving 90%, 88%, 87%, and 81% for bigram, trigram, 4-gram, and 5-gram, respectively

Arabic script web page language identifications using decision tree neural networks

In this paper, we propose a hybrid approach of Arabic scripts web page language identification based on decision tree and ARTMAP approaches. We use the decision tree approach to find the general identities of a web document, be it an Arabic script-based or a non-Arabic-based. Then, we use the selected representations of identified pages from the decision tree approach as an input to the ARTMAP neural network for further verification of the diversity of languages detected by the algorithm. From our initial experiments, we found that, although the decision tree approach may achieve a higher accuracy than ARTMAP, the former may not be as reliable as the ARTMAP approach if the language used is extended to other types of Arabic script web documents in different languages (e.g., Urdu, Arabic, Persian, etc.). Therefore, we propose this hybrid decision tree-ARTMAP approach in order to improve the performance of the Arabic script language identification on web documents in a variety of languages. The result shows that the proposed approach has outperformed both decision tree and the default ARTMAP approaches