## 1 Introduction
### Named Entity Recognition Techniques  
NER is a crucial component of information extraction, the foundation for constructing knowledge graphs, and the primary method for discovering a domain's knowledge ontology, as well as the fundamental task of building a knowledge graph through the extraction of specific categories and critical information from a variety of unstructured texts.  
Computer technology has contributed to the categorization of NER techniques into three distinct types: rule-based, statistical machine learning, and deep learning approaches. Firstly, the rule-based method of NER [9] requires specialists to manually spend a lot of time creating the rule templates [10] used to select entities that match or not in the early stages. Currently, the rule-based methods were applied to NER of various languages, including Korean [11], Arabic [12; 13], Persian [5], Chinese [14], Turkish [15], and others. It is noteworthy that the rule-based model achieved F-Score of 89.5% in Arabic NER [13] and 85.93% in Persian NER [5]. This approach is suitable for languages in which the rules are not particularly evident. The rule-based method has a high accuracy but a weak generalization ability. It fits to better deduce from the context of the entity made up by proper names and grammatical terms. Moreover, this method will have ambiguities because word polysemy causes completely distinct formulations to result from different contexts. Therefore, this approach is not suitable for named entity recognition for complex data.  
Secondly, statistical machine learning techniques like the HMM [16], CRF [17], SVM [18; 19; 20], and others regard NER as a classification question. This method performs better on small datasets but poorly on complex and huge datasets, and it calls for a feature matrix that has been predefined by experts. This method treats the entity recognition problem as a character label classification problem, and it uses labeled datasets and predetermined features to guide computers how to recognize entities in strings. As an example, HMM has been used in NER of Korean [21; 22], Chinese, and Urdu [23]. [PERSON] et al. [23] classified Urdu into person names, location names, organization names, and so on based on the HMM, precision, recall, and f-measure values of 55.98%, 83.11%, and 66.90%. [PERSON] et al. [24] did an experiment on software entities such as system, use case, and actor on based on an SVM model that achieved 72.1% for the F-Score. Statistical machine learning-based approaches have a fairly narrow range of applications, as they are only relevant to small datasets and have poor performances on datasets with complex entity types and vast amounts of data.  
Thirdly, deep learning models consisting of many stacked neural networks are widely used in NER, such as RNN [25; 26], Long Short-Time Memory (LSTM) [27], Bidirectional LSTM [28; 29], BiLSTM-CRF [30; 31], BERT-BiLSTM-CRF [6; 32], ALBERT-BiLSTM-CRF [8], and attention mechanisms [33], which have replaced statistical machine learning techniques. This method can handle high-dimensional data with excellent accuracy. Deep learning is utilized the most frequently in Korean [34; 35], Polish [36], Chinese [37], Arabic [38], and so on. The F1 value of the NER algorithm, the BI-LSTM-CRF model, on weaponry equipment names in Chinese reached 93.88% [37]. Based on the review of NER methods, the approach of stacking deep learning models was reliable in terms of accuracy and capability for processing complicated data.