# LLM-Enhanced Framework for Building Domain-Specific Lexicon for Urban Power Grid Design
## 1 Introduction  
Traditional methods for power grid design lack dynamic knowledge-updating mechanisms, making it challenging to adapt to business scenarios requiring multi-energy integration and high resilience. Meanwhile, the deep integration of big data in power systems with artificial intelligence technologies offers new perspectives for industry innovation. However, the effective utilization of power domain knowledge still faces critical bottlenecks: existing industry terminology standards struggle to cover all domain-specific vocabulary, including colloquial terms, alternative names, and abbreviations, along with issues like ambiguous definitions and delayed updates. These challenges significantly restrict the semantic parsing capabilities of intelligent design systems [1]. In this context, as the semantic cornerstone of knowledge-intensive systems, the quality of domain-specific lexicons directly impacts the depth of understanding by intelligent systems of professional knowledge. Particularly in the power sector, a critical national infrastructure domain, constructing highly accurate domain-specific lexicons has become a core requirement for supporting the intelligent transformation of power grids. Domain-specific lexicons serve as semantic analysis tools applicable to specific domains, compiling and distilling vocabulary from domain-related corpora to convey characteristic information of the field. The development of domain-specific lexicon construction techniques has undergone three primary stages [2]: manual methods, statistical methods, and deep learning-based methods. Early-stage lexicon construction mainly relied on expert knowledge and manually compiling entries, which typically resulted in high-quality lexicons but at the cost of significant time and human resources. Statistical methods leveraged large-scale corpora by automatically extracting terminology through calculating co-occurrence frequencies and other statistical features between words. However, these methods heavily depend on high-quality corpora and struggle with handling complex linguistic phenomena such as polysemy and collocations. Deep learning methods utilize neural networks to automatically learn word representations and incorporate contextual information for term recognition and classification. Nonetheless, this approach requires vast amounts of annotated data for model training and high computational resource demands, and it suffers from poor model interpretability. Despite these challenges, deep learning methods have provided new ideas and technical means for domain-specific lexicon construction, driving further advancements in the field. Domain-specific lexicons are widely used in data mining across various fields, such as accident disaster domains [3], social safety event domains [4], judicial domains [5], etc. The construction of domain-specific lexicons primarily involves three stages [6]: (1) Pre-processing: this includes converting file formats and denoising texts for Chinese corpora. (2) Seed Vocabulary Extraction: after segmentation, the results are evaluated, and word strings that do not meet the criteria are filtered out to obtain seed vocabulary. (3) Synonym Mining: by calculating word vector similarities, synonyms of seed vocabulary are discovered to expand the domain-specific lexicon. As the foundational component of a domain-specific lexicon, the quality of seed vocabulary directly impacts the effectiveness and application scope of the final lexicon. Its construction methods have evolved through three paradigms:  
(1) Rule-based Methods: Early studies utilized manually defined regular expressions, grammar rules, or statistical thresholds to screen candidate terms, such as the TF-IDF weighted model [7], which selects vocabulary based on the distribution characteristics of terms within document sets. While these methods offer interpretability advantages, they rely heavily on manually designed feature rules, making it challenging to adapt to vast and complex semantic scenarios.