# A Local Information Perception Enhancement-Based Method for Chinese NER
## 1 Introduction  
From a given unprocessed text, the named entity recognition (NER) task seeks to identify and categorize related entities. Named entity recognition has an essential effect in subsequent natural language processing (NLP) projects. These projects include relation extraction [1], question-answering systems [2], and entity linking [3]. The NER aspect of Chinese usually uses character-level annotation strategies to identify named entities [4]. Several studies have shown that the character-based NER approach avoids errors in the subword stage [5, 6]. However, sometimes the lexical boundary is the entity boundary; thus, the lack of boundary information provided by the lexicon may cause the wrong entity to be extracted. Take this one, for instance: \"Nanjing Yanqtze River Bridge\"; if there is no lexical knowledge, some wrong information, such as \"Mayor of Nanjing\" and \"[PERSON]\"), may be extracted. Therefore, recent research has focused on improving NER's performance by better integrating lexical information into characters. To our knowledge, there exist two primary methodologies for integrating character and lexical information. The first is the dynamic framework method. It designs corresponding structural support for lexical typing, such as Lattice-LSTM [7], LR-CNN [8], and FLAT [9]. Lattice-LSTM extends the commonly used character-based long short-termmemory (LSTM) networks to encode character information in sentences while fusing potential word information. The LR-CNN model employs convolutional neural networks (CNNs) to encode both character attributes and probable word features. Additionally, attention mechanisms are utilized to effectively integrate the information from characters and words. However, both RNNs and CNNs have limitations in modeling long-range dependencies [10]. FLAT overcomes this limitation by designing an ingenious positional encoding to fuse the lattice structure at the top of the Transformer [10]. As a result, FLAT can interact immediately with all matching words for characters independent of long-range dependencies. Despite the research progress, the above methods still need to improve the specific structure of neural networks, thus limiting the broader application. Another approach is constructing adaptive embedding based on lexical information, i.e., embedding lexical knowledge in the encoding stage. WC-LSTM uses four encoding strategies to encode the Lattice-LSTM input statically [11]. WC-LSTM, although an adaptive embedding paradigm, suffers from information loss. To incorporate contextual information in the original vector of individual characters, [PERSON] first filters the set of candidate entities for a given character and then constructs a character-entity relationship graph of characters and candidate entities [12]. The character representations in the character-entity relationship adjacency matrix are updated using graph attention networks (GAT). Finally, a character representation incorporating semantic information of contextual entities is obtained. To better utilize the lexical sources, SoftLexicon directly maps word characters to four positions, begin, middle, end, and single, and then uses a static weighting method to weight the word frequency magnitude in the lexical set [13]. SoftLexicon has been shown experimentally to effectively address the underutilization of low-speed inference and matching words, compensating for the shortcomings of the lattice-based model [14]. The unique feature of this approach is that it does not require the development of complex sequence modeling architectures. Therefore, it can be applied to other sequence annotation frameworks. In addition, some studies have achieved good results without utilizing an external lexicon. [PERSON] found that most types of entities have strong naming regularity. To effectively explore the internal compositional information of entities, a Regularity-Inspired reCOgnition Network (RICON) was designed [15]. The model utilizes a regularity-aware module to capture the internal regularity of each span.