## 2 Related Work
### Deep Learning-Based Methods  
With the development of deep learning technology, it can capture features layer by layer of network [17] to improve the accuracy of learning. Deep learning techniques with superior performance have been frequently applied in various natural language processing work, and show significant advantages in entity-linking tasks. [PERSON] et al. [18] proposed an entity-linking method based on deep neural networks, which utilized deep neural networks to learn entity and context representation and achieved superior performance without manually designed features. Since graph convolutional networks make entity association modeling easier, several models [19; 20; 21] employed a graph convolutional network to extract contextual features to improve the performance of entity linking. Moreover, the neural network needs further optimization for the semantic sparsity problem of short texts. Since LSTM (Long Short-Term Memory) has shown advantages in image recognition and other fields [22], [PERSON] et al. [23] proposed a short-text entity-linking method based on a double-attention-based LSTM network. However, the LSTM modeling could not capture both past and future contexts and had a large number of parameters. Then, a symmetric Bi-LSTM model with a dual attention mechanism was proposed by [PERSON] et al. [24], in which Bi-LSTM could better capture bi-directional semantic dependence, and their proposed model combined structural information with the attention process to more completely extract semantic properties of entities. Furthermore, [PERSON] et al. [25] proposed the ELMo model to extract the deep contextual representation, which captures semantic features of words and sentences using a multi-layer BiLSTM model. Additionally, [PERSON] et al. [26] adopted the ELMo model and attention mechanism to obtain the mention embedding. The abovementioned deep learning algorithms have achieved satisfactory results in entity linking. However, as some pre-trained models based on transformer architecture have outperformed previous deep learning models on major tasks, many researchers have turned their attention to transformer-based models. Firstly, [PERSON] et al. [27] presented the BERT model, which demonstrated excellent performance across a number of natural language processing tasks and was widely employed in entity linking. Then, in the entity linking task, [PERSON] et al. [28] exploited BERT-based entity embedding to better capture the type information of entities, which was applied to modeling contextual information of entities in knowledge bases. Recently, transformer-based models are also applied for entity linking in Chinese short texts. [PERSON] et al. [29] developed the BERT-ENE model based on BERT and treated the short textual entity-linking problem to a binary classification task. However, they ignored the assistance of keywords for entity linking in the context of candidate entities. Consequently, [PERSON] et al. [30] employed the TextRank keyword extraction technique in BERT to enhance the effectiveness of Chinese short-text entity linking. Nevertheless, these methods only utilized BERT models to extract semantic features that were not combined with other models to further explore the correlation between entities. [PERSON] et al. [31] proposed an approach to integrate BERT with a graph model, in which the graph model can capture the association between candidate entities with different mentions. In addition, [PERSON] et al. [32] applied the features extracted by BERT through a dual-channel network to further extract semantic information. However, excessive model superposition increases the computational complexity and sacrifices more computing resources while improving efficiency. Further enhancements to the performance and application of transformer-based models have been proposed subsequently. First, [PERSON] et al. [33] proposed a robust and optimized version of the RoBERTa model based on BERT, which outperformed BERT in multiple problems involving natural language processing.