# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
## 3 Model
### Idcnnlr  
There has been numerous research on entity recognition based on Bidirectional LSTM (BiLSTM), which can extract more sequential information to capture the semantic relationships between words. [PERSON] et al. [30] used BILSTM to extract the time sequence information of texts and built a BERT-BILSTM-CRF entity recognition model to realize entity recognition of texts in educational emergencies. [PERSON] et al. [31] add BILSTM and Transformer to the entity recognition model. These two modules improve the semantic extraction capability of the feature extraction layer. However, BiLSTM cannot fully utilize the computational power brought by GPU parallelism, thus being unable to significantly improve the model performance. Each step of the CNN calculation is relatively independent for the GPU, and each step of the CNN calculation can be carried out at the same time, so as to achieve parallel computing. Because of the different calculation methods, LSTM and other modules do not have this feature. CNN has also been widely applied in entity recognition research, as it possesses strong capabilities in extracting local features to capture the semantic relationships between words. Since CNN can only extract local semantic information, many studies increase the depth of the CNN network to extract broader semantic information, which may lead to overfitting issues. Pooling layers have been used to aggregate text information from multiple convolution scales, which can lead to the loss of necessary information. To address these challenges, [PERSON] et al. [32] proposed an Induced Dilated Convolutional Neural Network (IDCNN). IDCNN replaces the ordinary CNN with dilated convolutions, enlarging the receptive field of the convolution kernels without changing the number of model parameters. This allows the convolutional layers to learn global semantic information from the text. Figure 2: The internal structure of the multi-head attention mechanism. The internal structure of the IDCNN model is four layers of DCNN, where each DCNN layer contains two standard convolutions and one dilated convolution with a dilation value of 2. Before each convolutional layer, the LeakyReLU function is applied to ensure that the input data remains within an appropriate range. Following the activation function layer, a Layer Normalization (LayNorm) layer is added to prevent the gradient vanishing problem after several convolutional layers. The internal structure of DCNN is shown in Figure 3. The convolutional layers of DCNN are one-dimensional, and the data is propagated forward through four layers of DCNN sequentially. IDCNN swaps the second and third dimensions of the input data and then swaps them again to obtain the output vector. The paper improved the internal structure of the classical module IDCNN and obtained IDCNNLR module. Compared with IDCNN module, the extraction capability of IDCNNLR module has been enhanced to some extent. The data is sequentially propagated forward through four layers of DCNN of the IDCNN model, and these layers extract text features at different depths. IDCNNLR module does residuals summation of the output features for four layers of DCNN, and obtains the fusion features at different text depths. The information contained in features at different depths is non-overlapping, and fusing features from different depths provides the model with more semantic information. The internal structure of IDCNNLR is shown in Figure 4.  
The ReLU activation function can address the gradient vanishing problem in LSTM. It sets the gradient of negative input to 0, preventing the update of these parameters, and sets the gradient of positive input to 1, allowing the update of data gradients, ensuring that the model parameters are not difficult to update with low gradients. However, ReLU sets the gradient of negative values to 0, making it difficult for negative values to be updated, resulting in suboptimal training performance. To address this issue, IDCNNLR replaces the ReLU activation function with the LeakyReLU activation function.