# Named Entity Recognition Method Based on Multi-Feature Fusion
## 3 Research Design
### Stage 2: Named Entity Extraction Model
#### 3.3.2 Feature Extraction Layer  
BiLSTM  
BiLSTM (Bidirectional Long Short-Term Memory) is an improved version based on the LSTM model, which makes up for the limitation of the traditional LSTM that only focuses on forward information. In BiLSTM, sequence data is input into two LSTM layers, one for forward information and the other for reverse information. The outputs in both directions are usually combined by concatenation or addition to form a more comprehensive feature representation. Due to its ability to capture bidirectional dependencies in sequences, BiLSTM performs particularly well in tasks that require context information. Especially in natural language processing tasks, language has obvious bidirectional dependencies, and BiLSTM can better understand the semantic connections in sentences. The basic unit for implementing BiLSTM functions is LSTM. A standard LSTM unit contains four main components: forget gate, input gate, cell state and output gate. Its structure is shown in Figure 3.  
Figure 2: BERT structure diagram. The forget gate \(\mathrm{f_{f}}\) of LSTM is responsible for controlling which information will be forgotten, thereby selectively deleting information that is no longer needed; the input gate \(i_{t}\) is used to control the input information at the current moment and determine which new information will be introduced into the current unit; the output gate \(\mathrm{o_{f}}\) controls the information output of the current unit and transmits the processed information to the next moment; the short-term memory \(\mathrm{h_{f}}\) records the hidden state at the current moment and reflects the output information at the current moment; and the long-term memory \(\mathrm{c_{f}}\) saves the state of the current unit and serves as the input of the next moment to ensure that important historical information can be continuously transmitted; Sig represents the Sigmoid function; \(\tanh\) is the hyperbolic tangent activation function; \(\otimes\) represents the Hadamard product; and \(\oplus\) is the addition operation of the vector. The expressions are as follows:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{3}\]  
\[\mathrm{f_{f}}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{4}\]  
\[\mathrm{o_{f}}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[\mathrm{s_{t}}=\tanh(W_{s}\cdot[h_{t-1},x_{t}]+b_{s}) \tag{6}\]  
\[\mathrm{c_{f}}=f_{t}\otimes c_{t-1}+i_{t}\otimes s_{t} \tag{7}\]  
\[\mathrm{h_{f}}=o_{t}\otimes\tanh(c_{t}) \tag{8}\]  
Among them, \(\sigma\) represents the Sigmoid function; \(W\) is the weight matrix; \(b\) is the bias vector; \(x_{t}\) is the input of the current unit; and \(s_{t}\) is the updated state at the current moment. DGCNN  
DCNN (Dilated Convolutional Neural Network) is a neural network model that enhances feature extraction capabilities by using dilated convolution (also known as hole convolution). Compared with traditional convolutional neural networks (CNN), DCNN introduces a dilation rate, which enables certain data points to be skipped during the convolution process, thereby expanding the receptive field. The advantage of this is that a wider range of contextual information can be captured without increasing the size of the convolution kernel or more parameters. The sampling diagrams of ordinary convolution and hole convolution are shown in Figure 4.  
Figure 3: LSTM structure diagram. IDCNN enhances the information extraction capability of the model by stacking multiple DCNNs with the same structure. However, stacking multiple convolution blocks will lead to the problem of gradient vanishing. Therefore, this article refers to the Dilate Gated Convolutional Neural Network (DGCNN) proposed in reference [25], which introduces the residual structure.