# Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
## 1 Introduction  
The Named entity recognition (NER) is important in natural language processing (NLP) tasks used to detect named entities (NEs) in texts and classify them into predefined categories, such as location, person, time, date, and organization [1]. NER is a crucial preprocessing phase in various NLP applications to improve the overall performance. It extracts valuable information from raw data and simplifies downstream tasks, such as text clustering, information retrieval, translation, and question answering [2]. In recent years, Arabic NER (ANER) has become a challenging task and is receiving increasing attention from current researchers due to the limited availability of annotated datasets [3]. Arabic is a Semitic and the standard language spoken in the Arab world. The language is used in the Middle East, the Horn of Africa and North Africa, and it is used in the United Nations as one of the five official languages. In the Arab world, around 360 million people speak Arabic in more than 25 countries [4]. The Arabic NLP has drawn attention in recent years. Several NLP tasks are tricky, such as NER [5], particularly language important features, such as high morphological uncertainty of meaning, writing style, doubtfulness to the meaning of common words/proper noun, and absence of capitalization [2]. The ANER systems are based on either one of two methods: one is based on handcrafted rules, particularly the NERA2.0 system [6], and the other relies on statistical learning such as in [7]. Each method, nonetheless, has its pros and cons. NER systems designed on rule-based primarily depend on manually crafted grammatical rules learned from linguists. Therefore, the maintenance of these systems is time-consuming and laborious, especially when the knowledge and background of the linguists are poor. By contrast, systems based on machine learning (ML) obtain patternsrelated to the NER task from the training set of samples automatically, thereby not requiring in-depth language-specific knowledge. These ML-based systems are superior to rule-based systems because they are adjustable and easy to update with minimum cost and time, provided a sufficient corpus. In recent years, neural networks have drawn much attention with various models being proposed. Researchers have combined different semi-supervised learning and deep neural networks (DNNs) to find an optimum solution to the NER task and other chunking tasks [8]. Contrary to ordinary ML methods, deep learning can concurrently learn representations, categorize patterns, and considerably reduce the difficulty in NER tasks. Moreover, current deep learning models generally utilize word embeddings, which allow them to learn similar representations for semantically similar words. However, out-of-vocabulary (OOV) words, which are words that do not have any corresponding representation in the word embedding model, are difficult to handle especially for the Arabic language because of the limitation in resources. These words are also set randomly to a specific value. Therefore, we fully utilize the character representations of a token to label those OOV words. We also introduce the embedding attention layer that works as a gating mechanism that allows the model to dynamically learn what features are important. ANER is considered a sequence labeling task. Recurrent neural network (RNN) is a natural choice to tackle problems with sequential structure, such as NER, due to their ability to memorize previous values and correlate to other parts of a sequence. RNN surpasses other methods in terms of NER performance and other sequence labeling problems for many languages. To the best of our knowledge, no work has been done to address the ANER problem using RNN techniques, and most existing works are based on feature engineering and statistical methods. In this work, we propose a new method that has not been investigated in much detail for the Arabic language to date.