# Research on Chinese Named Entity Recognition Based on Lexical Information and Spatial Features
## 2 Related Work  
With the development of deep learning, researchers have begun applying neural network models to Named Entity Recognition (NER) [12]. There is a significant difference between Chinese named entity recognition and English named entity recognition. These differences include the following aspects: firstly, in English, there are natural spaces as separators between words, while in Chinese, there are no clear boundaries between characters. In Chinese entity recognition, there are already vocabulary-based or character-based methods. For instance, [PERSON] et al. [13] proposed a character-based Chinese entity tagger, proving the superiority of character-based methods over vocabulary-based methods. However, character-based methods do not utilize lexical information. Therefore, [PERSON] et al. [12] introduced a lattice structure, which matches characters with dictionaries to obtain matching vocabulary containing characters, and fused these matching vocabulary information with characters. This process helps to partition the boundaries of entities, thereby improving the accuracy of naming entity boundaries. [PERSON] et al. [14] also achieved good results in using vocabulary enhancement methods for named entity recognition in classical Chinese. Additionally, [PERSON] et al. [15] used Bi-LSTM to fuse the phonetic features of words with word features, reducing the impact of noise on NER in English social media datasets. Although the methods above have achieved good results in NER, LSTM requires information from the previous time step's hidden units, limiting the full utilization of GPU parallelism, and there might be conflicts between matched vocabularies. [PERSON] et al. [9] introduced a Convolutional Neural Network (CNN)-based Named Entity Recognition (NER) approach that incorporates a vocabulary feedback mechanism to tackle the aforementioned two challenges. On the one hand, CNN can leverage GPU parallelism for increased efficiency; on the other hand, CNN reanalyzes matched vocabularies, refining the network with feedback on high-level features to supplement low-level features, thus alleviating vocabulary conflicts. Moreover, due to shortcomings in the method designed by [PERSON] et al. [10], each character could only acquire information about vocabulary ending with it. For example, in the sentence (Chongqing Renhe Pharmacy), the character (\"medicine) could only obtain information from the vocabulary (\"  
capture various semantic and boundary relationships between different semantic units through adjacency matrices by transforming lattice structures into a unified graph, reducing excessive dependence on word information. With the continuous deepening of research, [PERSON] et al. [18] combined multiple feature fusion such as words and word roots to enhance the semantic information of sentences. In addition, [PERSON] et al. [19] utilized rule information by observing the internal rules of entities while avoiding excessive attention to cross internal regularity. [PERSON] et al. [20] conducted computational and qualitative analysis on the audience's experience during the competition, using social network-based modeling techniques and thematic analysis, respectively, to focus on emotional changes in the audience. [PERSON] et al. [21] considered pronunciation issues in Chinese, such as polyphonic characters or characters with the same pronunciation but different characters, introducing speech features through cross functions to enhance Chinese named entity recognition. Additionally, some researchers focus on extracting deeper features based on existing features. For example, [PERSON] et al. [22] introduced a Convolutional Attention Network designed for Chinese named entity recognition (NER). This approach leverages Convolutional Neural Networks (CNNs) to capture proximate character relationships and employs GRU to capture sentence-level contextual information. This further indicates that there are certain local spatial relationships between characters and vocabularies, and these relationships are beneficial for NER.