# A Local Information Perception Enhancement-Based Method for Chinese NER
## 1 Introduction
Then, a regularity-agnostic module is employed to mitigate the excessive focus on span regularity. RICON achieves the state-of-the-art performance of the year on the four datasets. [PERSON] utilized BERT pretrained language models to replace traditional static word embeddings [16; 17]. Employing a context-dependent dynamic generation of semantic vectors improved the representation of word embeddings. It could extract entities more accurately and efficiently than traditional named entity recognition algorithms. It also achieved good results in the named entity recognition task within history and culture. To reduce the dependence on data annotation, [PERSON] developed a new semisupervised model called MAUIL [18]. Compared with other models, MAUIL cleverly integrates multiple levels of attribute embedding, such as character-level and word-level features. This approach enhances the high-level semantic features in text and dramatically improves the reliability of artificial intelligence programs, such as named entity recognition. In addition, [PERSON] proposed a new method called W2 NER, which can handle three types of NER tasks: planar entities, overlapping entities, and discontinuous entities in a unified manner [19]. The NER task is constructively transformed into predictive word-word relation classification. The model structure effectively simulates the adjacency relationships between entity words using next neighbor word (NNW) and trailing head word-* (THW-*) relations. W2 NER has driven unified NER to achieve the most advanced performance. These proposed new frameworks bring new ideas to Chinese NER. According to our findings, most existing studies focus on entity discovery methods. These methods focus more on detecting entity boundaries and only consider words in the thesaurus that match entity characters. However, they ignore the knowledge of the interaction between entity characters and their neighboring matching characters. Fusing lexical information improves the representation for kanji, and this is necessary for Chinese NER. However, information on the entity boundary region is essential for entity detection, and existing lexicon-based methods pay less attention to this region. Our proposed boundary region is the adjacent region's front and back zones of the entity boundary, as shown in Figure 1. It is a boundary region of size K, which we call Zone-K.  
On the one hand, the lexical semantics of Zone-K helps to improve the understanding of entities and thus to determine their categories. For example, Figure 2 shows that although \"\(\%\)\" (Kaohsiung)\" can be detected in both sentences 1 and 2, it is a challenge to determine its category as \"PER\" in sentence 1 and \"LOC\" in sentence 2. This is because there are many polysemous words in Chinese. Thus, even if the boundary of an entity can be detected correctly, determining its category is still a challenge. In this case, we propose considering the semantics of Zone-K characters and their lexical matching words. For example, in sentence 1, the category of \"\(\%\)\" (Kaohsiung)\" can be identified as \"PER\" by \"\(\%\)\" (performer)\" and \"\(\%\)\" (play)\". In contrast, in sentence 2, the category of \"\(\%\)\" (Kaohsiung)\" can be identified as \"LOC\" by \"\(\%\)\" and \"\(\%\)\" (live)\". Second, there is a significant semantic change between the characters in Zone-K and the boundary characters of the entity. For the example, in Figure 3, for the sentence \"\(\%\)\" (I stayed in Kaohsiung for a few days)\", since the two character sequences \"\(\%\)\" (in)-\(\%\) (gao)\" and \"\(\%\) (xiong)-\(\%\) (live)\" are small probability co-occurrence sequences, we consider \"\(\%\)\" (in)-\(\%\) (gao)\" and \"\(\%\)\" (xiong)- (\% (live)\" as two semantic violators, which means that the semantic distance between \"\(\%\)\" (in)\" and \"\(\%\)\" (gao)\" as well as \"\(\%\)\" (xiong)\" and \"\(\%\)\" (live)\" is quite far.