# A Local Information Perception Enhancement-Based Method for Chinese NER
## 1 Introduction
Therefore, we consider Zone-K as a semantic mutation zone, which is similar to an image's contour and reflects the text's local feature discontinuity. Semantic changes in Zone-K can help determine the entity's boundary \"\(\%\)\" (Kaohsiung)\". In summary, we propose to use the Zone-K information in two ways. One is to fuse the lexical knowledge of Zone-K to help determine the category of entities. For this purpose, we propose to use graph attention networks to catch connections among characters with their neighboring character-matching words. For example, based on semantics of the adjacent contextual match \"\(\%\)\" (play)\" for \"\(\%\)\" (xiong)\", \"\(\%\)\" (Kaohsiung)\" can be inferentially tagged as \"PER\". Second, the semantic transformation of Zone-K is introduced to help determine the boundaries. This is similar to contour detection in images, where local semantic fusion  
Figure 1: The front zone and back zone of the entity. Figure 3: Examples of front and back zones of an entity. can detect semantic change boundaries. For this purpose, we introduce CNN, which uses sliding windows to fuse short-sequence information of the text to perceive local sequence features of the text. Furthermore, [PERSON] and [PERSON] proposed to combine LSTM and CNN networks to learn character-word level information for English NERs [20]. This inspired us to provide a model that combines short-sequence CNN and LSTM coding. The local features of the text are extracted using short-sequence CNN, resulting in a local contextual representation. The global context representation is obtained by implicitly encoding the character sequence using LSTM, and then the local and global representations are used together for NER. Our approach bridges the gap in the following aspects compared with previous approaches. First, the model adopts feature representation, context encoder, and tag decoder architecture, which has the property of migrating to other networks. Moreover, it can be combined with BERT [17]. Second, we introduce lexical information from the character representation layer based on the SoftLexicon method, which is simple and direct. The graph neural network is used to directly capture the lexical semantic information of entity neighborhoods without the need to construct dependency parse trees with the help of external NLP tools. It effectively avoids error propagation issues, makes up for information loss, and improves the performance of Chinese NER. Finally, the sequence coding layer of the model effectively balances the acquisition of local and global information and enhances the recognition of entity boundaries. Adding GAT simply and effectively improves the prediction accuracy of entity types. The present study can be succinctly outlined by considering the following facets:  
1. We constructed a Chinese NER method with enhanced local information perception. The method directly utilizes local lexical information to capture the semantic relationship between entity characters and matching lexical entries through graph attention networks. There is no need to build dependency parse trees with the help of external NLP tools. This avoids the problem of error propagation caused by this process, thus effectively improving NER performance. 2. We used a modified short-sequence CNN to fuse local features and achieve encoding of shorter subsequence features by an additional sliding window module. Then we combined it with LSTM to obtain a global representation of the sequence. It compensates for the shortcomings of existing sequence encoders in extracting local and global features. 3. The experiment achieved advanced results on standard Chinese NER datasets in four domains. Moreover, entity-type prediction accuracy improved, indicating that the proposed local information-aware approach is interpretable.