# LLM-Enhanced Framework for Building Domain-Specific Lexicon for Urban Power Grid Design
## 1 Introduction
(2) Statistical Learning Methods: Represented by N-gram models [8] and mutual information combined with adjacency entropy methods [9], these approaches capture co-occurrence patterns of vocabulary using probabilistic models. For instance, [PERSON] et al. [10] achieved a new term discovery accuracy rate of 76% by combining Jieba segmentation with statistical filtering for seed vocabulary extraction. (3) Deep Learning Methods: Utilizing Word2 Vec [11], BERT [12], and other embedding techniques, these methods capture semantic associations through distributed representations, enabling the identification of semantically similar words. However, deep models require large-scale annotated data, which conflicts with the scarcity of power domain corpora. Synonym expansion not only enriches the content of domain-specific lexicons but also enhances the model's understanding of complex semantic relationships within the domain. Synonym mining can be divided into two directions: synonym differentiation and synonym set extraction. Synonym differentiation aims to determine whether two given words are synonyms. Early research, such as [PERSON] et al. 's work [13], often relied on manually constructed lexical resources to query these resources and judge synonymy between words. [PERSON] et al. 's word2 vec model [14] maps words into a continuous vector space, where semantically similar words are closer. This method not only improves efficiency but also identifies synonyms without pre-defined vocabularies. Additionally, some studies combine knowledge graphs with deep learning methods; for example, [PERSON] et al. [15] proposed a neural network model incorporating knowledge graph information, significantly improving the accuracy of synonym differentiation. Synonym set extraction involves automatically discovering new synonym combinations from large corpora. Traditional approaches utilize co-occurrence analysis [16], calculating the frequency at which words appear together in the same context as a measure of synonymy. [PERSON] et al. [17] developed a Graph Neural Network (GNN)-based algorithm to extract synonym sets from text data, effectively capturing deeper semantic connections between words by modeling complex relationships. Some studies also explore unsupervised or semi-supervised learning strategies for synonym set mining [18] to reduce reliance on annotated data. In summary, although existing methods have achieved certain successes in constructing lexicons for general or some vertical domains, the power design domain exhibits significant uniqueness:  
(1) The terminology system in this domain is complex, encompassing both specialized compound terms like \"tower load\" and \"insulation coordination\", as well as cross-domain high-frequency terms like \"grounding\" and \"protection\". Traditional statistical models are susceptible to issues like corpus sparsity and semantic ambiguity. (2) Current synonym mining techniques lack targeted solutions for mapping synonyms among naming entities such as power equipment models and standard codes, which are core elements of the power design knowledge system. Therefore, the primary problems addressed in this study include the following:  
(1) How can one construct a high-quality seed vocabulary set for the power design domain? (2) How can one effectively handle complex linguistic phenomena, such as polysemy and collocations, in the power design domain based on the vocabulary set, thereby expanding it into a high-quality domain-specific lexicon? This research aims to enhance the accuracy and efficiency of constructing domain-specific lexicons for urban power grid design using Large Language Models (LLMs), as illustrated in Figure 1.  
The novelty of this research is reflected in the following:  
Unlike existing studies that often use statistical methods or LLMs independently, this study proposes a three-level collaborative architecture of \"statistics-extension-correction\".