# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 2 Related Work  
Generally, there are two types of methods for entity recognition: sequence-based methods and span-based methods. The sequence-based approach defines entity recognition as a sequence labeling problem. In this method, each character is labeled with one or more labels for entity recognition. Classical sequence labeling methods include the neural network model [4] (NNS), the hidden Markov model [5] (HMM) and conditional random fields [6] (CRF). With the wide application of deep learning in natural language processing, deep learning techniques have been applied to entity recognition tasks. The classic modules of deep learning include the convolutional neural network [7] (CNN), fully connected layer [8] (Linear), recurrent neural network [9] (RNN), long short-term memory network [10] (LSTM), and gated recurrent neural network [11] (GRU). For example, [PERSON] et al. [12] proposed the BILSTM-CRF model, which combines bidirectional LSTM and CRF to achieve entity recognition. The CNN lacks the extraction of sequence features, so [PERSON] et al. [13] combined the cyclic neural network LSTM and the convolutional neural network CNN to construct a LSTM-CNNs neural network structure. The model uses the CNN to extract local features from word-level and character-level features and uses BILSTM to annotate each element. [PERSON] et al. [14] proposed the BILSTMCNNs-CRF model, which combines a CNN, bidirectional LSTM, and CRF to construct a complete end-to-end neural network model for various sequence labeling tasks. For Chinese NER, [PERSON] et al. [15] proposed the Lattice-LSTM model, which incorporates Chinese word features into the model to enhance entity information. However, the Lattice-LSTM model is unable to determine the length and number of Chinese words, which may lead to problems such as difficult batch training and poor generalization ability. To solve these problems, [PERSON] et al. [16] proposed the Soft-Lexicon model based on Lattice-LSTM. This new model improves the running speed and generalization ability of the Lattice-LSTM model. [PERSON] et al. [17] proposed a convolutional attention network (CAN) model. The model adds an attention mechanism on CNNS and a global attention mechanism on GRNN to extract semantic and contextual information between characters. [PERSON] et al. [18] proposed a rethinking mechanism and integrated this mechanism into LR-CNN (lexiconrethinkingCNN). In this study, encoded information of binary and ternary words was added to achieve data enhancement. The span-based approach defines entity recognition as the classification problem of entity boundaries. Firstly, all possible entity boundaries are listed, and the semantic features of entity boundaries are extracted by the feature extraction layer. Finally, entities and entity categories are predicted by the entity prediction module. Compared with sequence -based methods, span-based methods do not require labeling of individual elements, making them suitable for nested entity recognition tasks. For example, [PERSON] et al. [19] proposed an exhaustive model that enumerates all possible spans and uses LSTM to predict the categories of generated spans. [PERSON] and [PERSON] [20] proposed a local detection method that encodes the span and its context into a fixed-size feature vector and uses the feature vector to predict the category of the corresponding entity. [PERSON] et al. [21] separated entity span extraction and entity type prediction. The study used the overall detector to obtain the possible entity span and then used the classifier to predict the class of the corresponding entity. [PERSON] et al. [22] proposed a unified NER framework based on word pair relationships. The model constructs a table of all possible entity spans and predicts the categories of these entity spans. A span-based approach can also be implemented by adding a network of pointers to the network model. Pointer networks can generate output sequences of variable lengths.