## 3 Method
### Model
#### 3.2.2 IDCNN Layer  
Dilated Convolutional Neural Networks (DCNN) [30] were initially applied in the field of image segmentation, and it mainly changes the use of convolutional kernels of CNNs. The DCNN expands the perceptual field of view by adding a width to the convolutional kernel, ignoring only the information on the convolutional kernel, and no longer performs convolutional operations on the continuous region of the input matrix like the ordinary CNN's convolutional kernel.  
As is shown in Figure 2(a), it is a \(3\times 3\) 1-dilated convolution, which convolves on a contiguous region of the input data like the general CNN convolution kernel; Figure 2(b) corresponds to a 2-dilated convolution, where the size of the convolution kernel remains unchanged, but the width is increased, so the perceptual field of view is widened as well. The 4-dilated convolution shown in Figure 2(c) increases the perceptual field of view while keeping the convolution kernel unchanged. In the three figures, only the data in the nine red dot regions are focused on, and the weight of the data in the middle part is set to 0.  
From our analysis, the convolutional operation of traditional CNN, when stride is set to 1, the three layers of \(3\times 3\) convolutional kernel can act in a range of \((kernel-1)\times layer+1=7\), and the perceptible area and the number of layers are linearly related. However, in DCNN, the convolutional kernel with width has an exponential relationship between the perceived area and the number of layers, and the perceived field of view expands rapidly. In this way, more information can be obtained with as few layers  
Figure 3: Schematic (**a**) is a \(3\times 3\) 1-dilated convolution (**b**) is a \(3\times 3\) 2-dilated convolution (**c**) is a \(3\times 3\) 4-dilated convolution diagram of DCNN.  
as possible, solving the problem that ordinary CNNs can only obtain a small amount of information.  
In the sequence labeling task, any word in the sentence may have an impact on the current word. CNN is to cover more information in the sequence by increasing the number of layers of convolutional layers, making the network structure more and more complex. Dropout, for example, is also used to prevent overfitting from bringing more hyperparameters, which makes the whole model training even more difficult. DCNN makes the perceptual field of view larger without increasing parameters, and solves the problem of too many parameters without information loss. Generally stacking Dilated CNNs can obtain more information, but this method leads to overfitting, so we choose IDCNN (Iterated Dilated CNN) as an alternative. IDCNN applies the same DCNN structure several times to increase the generalization ability of the model by reusing the same parameters in a cyclic manner. IDCNN structure is composed of four identical Dilated CNN blocks stitched together, where each Dilated CNN block has three convolutional layers inside with dilation widths of 1, 1, and 2, respectively.  
In the NER model proposed in this paper, sentences are first pre-trained by BERT-WWM to obtain word vectors, and then input to Iterated Dilated CNN. IDCNN extracts local feature information of sentences by convolutional operations, and the entire perceptual field of view grows exponentially due to the increased width of the convolutional kernel, which reduces a large number of features without losing parameters and increases the training speed of the model. The feature vector with local information obtained by IDCNN is used as the input to BiLSTM to further extract the features of the whole sequence. In the internal IDCNN structure, the word vectors obtained from BERT training are firstly input into the first layer of DCNN, and the results of the first layer processing are output to the second layer of DCNN and other DCNN layers, respectively, and finally the output feature vectors are spliced.