# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 3 Model
### BILTAR Module
#### 3.3.1 BILSTM Module  
The BILTAR module adjusts the dimension of the output vector to make the feature vector size suitable for the GlobalPointer module. The method of changing the dimension is as follows:  
Set the output vector dimension of the LSTM to change the last dimension of the feature vector to ent_type_size\"inner_dim\"2 and obtain a feature vector with a dimension of (batch_size, seq_len, ent_type_size\"inner_dim\"2). Split the last dimension of the eigenvector to obtain a feature vector with a dimension of (batch_size, seq_len, ent_type_size, inner_dim\"2). In the last dimension of the feature vector, take the first inner_dim values as the feature vector q, and the last inner_dim values as the feature vector k. The dimensions of q and k are (batch_size, seq_len, ent_type_size, inner_dim). The feature vectors q and k are the inputs of the GlobalPointer module. The BILSTM is used to extract sequence information from data, the TIME layer is used to encode features at each position of the sequence, the attention mechanism is used to denoise the data, the PN forward-backward module is used to refine the forward and backward sequence information, and the residual structure is used to fuse different features. The following describes each structure block of the BILTAR module in detail. The BILSTM is a classic neural network structure that processes sequence data. Text data are also sequence data, so the entity recognition task is very suitable for using BILSTM to obtain the sequence information of the text. RNN has a problem of long-term dependency and struggles to remember long sequence information. Therefore, the LSTM structure is used to increase the model's memory capacity for long-term sequences, so that the model can extract more long-time sequence information. There is an inevitable connection between the entity and the entity context, so BILSTM is used to obtain the sequence information of the entity context. At the same time, the forward and backward sequence information may not be the same. Therefore, LSTM is upgraded to BILSTM in order to obtain the forward and backward sequence information, so as to obtain the semantic information of the sequence from more dimensions. Figure 3 shows the structure diagram of the bidirectional recurrent neural network BILSTM. In this paper's model, the BILSTM is a one-layer structure. Taking the internal structure of a one-layer BILSTM as an example, this paper introduces the implementation principle of BILSTM. The single-layer BILSTM consists of two different LSTMs [30]. One layer of LSTMs is responsible for the forward propagation of the data to obtain the forward  
Figure 3: Bidirectional recurrent neural network BILSTM. information of the sequence. Another layer of LSTM is responsible for the backward propagation of the data to obtain the backward information of the sequence. The forward and backward information are concatenated to obtain the output vector of the BILSTM. The LSTM unit consists of an input gate \(i_{t}\), a forget gate \(f_{t}\), an output gate \(o_{t}\), and a candidate memory cell \(g_{t}\). Based on the input data \(x_{t}\) and the previous hidden state \(h_{t-1}\), the values of the three gates and the candidate memory cell \(g_{t}\) are calculated. The forget gate \(f_{t}\) and the candidate memory cell \(g_{t}\) are multiplied to delete some useless memory information. The input gate \(i_{t}\) and the candidate memory cell \(g_{t}\) are multiplied to obtain the necessary memory information. The sum of the two inner products is the memory cell \(c_{t}\) at this time step. After passing through the tanh activation function, the memory cell \(c_{t}\) is multiplied by the value of the output gate to obtain the hidden state \(h_{t}\) at this time step. The calculation formula for the LSTM unit is shown in Equation (7). \[