# Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
## 2 Related Work  
As per recent systematic review for NER conducted by [9], NER approaches can be categorized into three main categories rule-based, learning-based and hybrid approaches. Furthermore, another comprehensive survey conducted by [PERSON] [2], classify the statistical methods of Arabic NER into six main approaches: CRF, NB, HMM, ME, SVM and Neural network. Going more in-depth into deep learning, a survey on ANLP using deep learning techniques by [10]. The author reviewed many kindsof literature on various ANLP tasks and concluded that yet a considerable gap exists in the Arabic NLP compared to that in English NLP. Basic ML approaches rely on feature engineering, external gazetteers, and chunks. They can achieve excellent accuracy and performance but require a dedicated expert in the domain of knowledge and are time-consuming. Therefore, scholars have begun to consider the artificial neural network (ANN) and DNN methods. These approaches reduce the dependency on feature engineering and are thus less laborious and time-consuming. These methods have been adopted to many NLP problems successfully such as in [11]. The authors in [12] proposed an RNN model for the Chinese Bio-NER that detects two types of predefined annotations, namely, subject and lesion, which are two main parts of symptom entities. A real-world Chinese clinical dataset that includes 12,498 records was used. A priori word information (POS tags) was added for improving the final performance. The final F-scores for the subject and lesion detection reach 90.36% and 90.48% respectively. In [13], the authors proposed a hybrid NER system for the Russian language that uses a Bi-LSTM-CRF system for various kinds of DNN models experimented starting from vanilla (Bi-LSTM). The models were further supplemented with CRF along with highway networks and added with word embedding. He evaluated all the proposed systems across three datasets: [PERSON]'s, Person-1000, and FactRuEval 2016. He concluded that the quality of predictions is considerably increased with the extension of Bi-LSTM model with CRF and can be further improved by setting the word input to preprocessing with exterior word embedding. The authors in [14] proposed an improved NER system using deep learning module for Chinese text. Without using any manual feature engineering, the system can detect the word features automatically. He used word embedding with a Bi-LSTM obtained from the outputs of NER to model the substance within a sentence. Along with additional features to the model, the experimental results show that the model achieves an F-score of 0.9247 when trained on a large corpus on Bi-LSTM with word embedding. The authors in [15] investigated a deep learning method to recognize NEs from Chinese clinical text using the minimal feature engineering approach. Two DNN models were developed: one is for generating the word embedding, and the other is for the main NER task. They evaluated the system on two Chinese clinical datasets: one is an annotated corpus that contains 400 randomly selected admission notes, and the other is unlabeled admission notes that include 36,828 notes, both of which were collected from the EHR database of Peking Union Medical College Hospital in China. The model results indicate that the proposed approach with DNN outperforms the CRF and achieves a high F1-score of 0.9280. In addition to NER for other languages, such as English, Russian, Chinese, and Hindi, The ANER is an attractive and challenging task and requires a recognition task due to the peculiar and unique characteristics of the Arabic language. The authors in [16] adopted a new attempt for ANER using ANNs. They used ANN techniques and evaluated the performance of their model with decision trees. Their system consists of three main phases: preprocessing of the data, conversion of Arabic letters to Roman alphabets, and classification of the collected data using a neural network. The relationship between the accuracy of the system and the corpus size was also assessed.