# Research on Chinese Named Entity Recognition Based on Lexical Information and Spatial Features
###### Abstract  
In the field of Chinese-named entity recognition, recent research has sparked new interest by combining lexical features with character-based methods. Although this vocabulary enhancement method provides a new perspective, it faces two main challenges: firstly, using character-by-character matching can easily lead to conflicts during the vocabulary matching process. Although existing solutions attempt to alleviate this problem by obtaining semantic information about words, they still lack sufficient temporal sequential or global information acquisition; secondly, due to the limitations of dictionaries, there may be words in a sentence that do not match the dictionary. In this situation, existing vocabulary enhancement methods cannot effectively play a role. To address these issues, this paper proposes a method based on lexical information and spatial features. This method carefully considers the neighborhood and overlap relationships of characters in vocabulary and establishes global bidirectional semantic and temporal sequential information to effectively address the impact of conflicting vocabulary and character fusion on entity segmentation. Secondly, the attention score matrix extracted by the point-by-point convolutional network captures the local spatial relationship between characters without fused vocabulary information and characters with fused vocabulary information, aiming to compensate for information loss and strengthen spatial connections. The comparison results with the baseline model show that the SISF method proposed in this paper improves the F1 metric by 0.72%, 3.12%, 1.07%, and 0.37% on the Resume, Weibo, Ontonotes 4.0, and MSRA datasets, respectively. entity recognition; semantic information; word enhancement; Bi-LSTM; spatial features +
Footnote †: journal: MedicalAlthough Flat-Lattice uses vocabulary enhancement methods to integrate vocabulary features into character-based features, utilizing vocabulary information to enhance the ability of Chinese-named entity recognition, there are still two issues with this method. Firstly, Flat-Lattice compares the vocabulary in the sentence with the vocabulary in the lexicon character by character until the vocabulary in the sentence cannot find the exact matching vocabulary in the lexicon. In this process, all sub-matched words are added to the vocabulary sequence. However, the words matched by the method of character comparison may conflict, i.e., a single character in a sentence may correspond to characters in more than one matched vocabulary. If the conflicting matched vocabularies are fused with characters after encoding only, this may lead to the lack of semantic and sequential temporal information between the matched vocabularies. Instead of enhancing entity boundary delimitation, this may have an inhibitory effect. For example, in Figure 1, the character '\(\wedge\)' ('person') can match both '\(\blacksquare\)' ('Chongqing person') and '\(\blacksquare\)' ('Renhe Pharmacy'). The Flat-Lattice method, after encoding, directly merges these matched vocabularies with the character, which may mislead the model to identify '\(\wedge\)' as both 'E-LOC' and 'B-LOC'. More critically, conflicts between matched vocabularies are common in Chinese NER [8]. In order to solve the problem of conflict between matched vocabularies, researchers have applied various models. Among them, [PERSON] et al. [9] applied a CNN model to alleviate the conflict between matching words by extracting the semantic features of the matching words through the feedback method of rethinking mechanism. However, the convolutional operation used by CNN perceives the input locally, and each convolutional kernel can only perceive a fixed-size window of the input data. In addition, the convolution kernel is invariant, which means that the model uses the same convolution kernel at different locations. Therefore, the CNN model ignores the vocabularies' sequential temporal information and the matching vocabularies' global information throughout the sentence.