# A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text
## 1 Introduction  
Arabic is one of the six official languages of the United Nations (UN), which belongs to the Semitic languages used by Arabs and Muslims all over the world. The estimated Arabic speaking population in the world is around 400 million native speakers and 1 billion Muslims with 30 different dialects [1]. The Arabic language alphabet consists of 28 letters in addition to the Hamza. The orientation of writing in Arabic is from right to left, there is no capitalization in Arabic and Arabic letters change shape according to their position in the word [2]. In the Arabic language, diacritic marks are used to clarify how to pronounce a letter. These diacritics are written either above or below a letter within a word as shown in Table 1. Diacritization is important since it removes word ambiguity. For example, the word \(\omega\)_j_j_i_i_i_j_i_i_j_i_i_j_i_i_j_i_i_j_i_i_i_j_i_i_i_j_i  
lemmatizer [3]. Automatic diacritization enhances the performance of many applications in accordance with their accuracy and speed of processing and can be beneficial in other Arabic processing steps such as Part-of-Speech (POS) tagging. Nevertheless, diacritization suffers low accuracy as indicated in prior works [4]. This problem is considered challenging because many words in Arabic have different meanings subject to their diacritization [5] and correct diacritization requires analyzing the full sentence as it takes into account the context of the sentence to diacritize words. Natural Language Processing (NLP) is defined as \"an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things\" [6]. Applications of NLP include a number of interesting areas including sentiment analysis, machine translation, information retrieval, speech recognition and expert systems. Many machine learning algorithms have been used to provide an improved performance to NLP applications since the accuracy of some of the NLP applications is very crucial. It is worth understanding some basic aspects of text processing because high-quality text processing is the key to creating robust NLP applications and therefore, improve the way it is indexed or linked to other resources. Arabic NLP is gaining a lot of attention by researchers and the particular reason for this is that the Arabic language is being widely used in different applications and services. Processing text in the Arabic language and Arabic NLP applications present researchers and developers with serious challenges due to the linguistic structure, as it is considered a highly structured language that depends on derivatives and morphology [2]. To make the language more usable, many NLP tasks were applied to the Arabic language. Over the last decade, Arabic have begun to gain ground in the area of research within NLP. Much work targeted different aspects related to how this language is processed, such as: morphological analysis [7], lemmatization [3], text categorization [8], documents summarizing [9], word segmentation [10], sentiment analysis [11] and automatic diacritization [12]. Automatic text diacritization is essential for a range of applications including Text-to-Speech (TTS) [13], Automatic Speech Recognition (ASR) [14], machine translation [15], information retrieval [12] and much more. These applications have been integrated with deep learning to provide robust performance [16]. Roughly speaking, deep learning is a machine learning method based on neural network architectures with multiple layers of processing units that provide a hierarchical representation of the data by means of various convolutions. In deep learning, the neural networks have various (deep) layers that enable learning. The more deep learning algorithms learn, the better they perform. Deep learning has been widely used in different fields and has made great progress in the areas of computer vision, speech recognition and NLP [17].