# Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military
## 2 Related Works  
The concept of named entity recognition (NER) [5] was proposed at Message Understanding Conference-6, and the research work on NER has been developed for more than 20 years. Due to the inherent particularity and structural complexity of Chinese, Chinese NER research started later and was more difficult than English NER. In addition, MNEs have certain domain specificities and unique nomenclature, so the current research on identifying MNEs is not deep enough.  
Traditionally, the MNER systems are based on either one of two methods: One is based on handcrafted rules and a dictionary; the other relies on statistical methods, such as CRF, HMM and support vector machine (SVM) [6]. The first method constructs the rules by linguistic experts manually and assigns weights to each rule, and then determines the type according to the conformity of entities and rules. Based on the rules and a dictionary, [PERSON] et al. [7] adopted a custom semantic pattern to extract entities in tactical reports. Although the recognition accuracy was high, its compilation process was time-consuming. An approach based on rules is difficult to cover all the language phenomena and there are some problems such as poor portability, updating and maintenance difficulties. It requires linguistic experts to rewrite rules for different systems.  
The statistical-based method mines and analyzes the linguistic information contained in the training corpus by statistical models, and extracts features from the training corpus, including word features, context features, and semantic features. [PERSON] et al. [8] utilized the CRF model with a small granularity strategy to learn text features and then identified MNEs in operational documents. The authors in Reference [9] proposed a method based on rules and the CRF model to construct a semi-automatic MNER system. According to the grammatical characteristics of military text, the system established an efficient feature set to recognize MNEs. Furthermore, another statistical model conducted by [PERSON] [10], when training the CRF model, external dictionary features were added to the model to make up for the shortage of training data and improve the accuracy of recognition to a certain extent. However, this type of statistical model has a common problem. Handcrafted features and domain-specific knowledge resources (e.g., a manually annotated dataset) are needed as inputs to train these models. The recognition effect is very dependent on the quality of the selected features.  
With advances in deep learning, researchers have gradually applied the method of deep learning to high-performance MNER tasks, which can avoid cumbersome feature engineering. The deep learning method for sequence tagging is mainly to detect potential information in text by building a multi-layer neural network structure. Considering the constructive features of military equipment names, the author in Reference [11] put forward an identification method with the model of deep neural network based on the character of the word vector and state. In view of the difficulty of building feature templates in military texts, [PERSON] et al. [12] proposed an entity recognition framework based on BiLSTM-CRF. They combine the word and character vector as input to improve the performance of the model. The experimental results show that the proposed method is feasible and effective, and the F1 value on the test corpus reached 87.38%. [PERSON] et al. [13] adopted an architecture based on CNN-BiLSTM-CRF for identifying MNEs of combat documents. They used a convolutional neural network (CNN) to obtain the character embedding and achieve an F1-value of 89.89% on the test corpus.