Applied Science, Faculty of
Non UBC
Electrical and Computer Engineering, Department of
DSpace
Applied Sciences 15 (8): 4134 (2025)
Xu, Yan
Wang, Tao
Yuan, Yang
Huang, Ziyue
Chen, Xi
Zhang, Bo
Zhang, Xiaorong
Wang, Zehua
Traditional methods for urban power grid design have struggled to meet the demands of multi-energy integration and high resilience scenarios due to issues such as delayed updates of terminology and semantic ambiguity. Current techniques for constructing domain-specific lexicons face challenges like the insufficient coverage of specialized vocabulary and imprecise synonym mining, which restrict the semantic parsing capabilities of intelligent design systems. To address these challenges, this study proposes a framework for constructing a domain-specific lexicon for urban power grid design based on Large Language Models (LLMs). The aim is to enhance the accuracy and practicality of the lexicon through multi-level term extraction and synonym expansion. Initially, a structured corpus covering national and industry standards in the field of power was constructed. An improved Term Frequency–Inverse Document Frequency (TF-IDF) algorithm, combined with mutual information and adjacency entropy filtering mechanisms, was utilized to extract high-quality seed vocabulary from 3426 candidate terms. Leveraging LLMs, multi-level prompt templates were designed to guide synonym mining, incorporating a self-correction mechanism for semantic verification to mitigate errors caused by model hallucinations. This approach successfully built a domain-specific lexicon comprising 3426 core seed words and 10,745 synonyms. The average cosine similarity of synonym pairs reached 0.86, and expert validation confirmed an accuracy rate of 89.3%; text classification experiments showed that integrating the domain-specific dictionary improved the classifier’s F1-score by 9.2%, demonstrating the effectiveness of the method. This research innovatively constructs a high-precision terminology dictionary in the field of power design for the first time through embedding domain-driven constraints and validation workflows, solving the problems of insufficient coverage and imprecise expansion of traditional methods, and supporting the development of semantically intelligent systems for smart urban power grid design, with significant practical application value.
Academic Editor: Len GelmanReceived: 27 February 2025Revised: 3 April 2025Accepted: 6 April 2025Published: 9 April 2025Citation: Xu, Y.; Wang, T.; Yuan, Y.;Huang, Z.; Chen, X.; Zhang, B.; Zhang,X.; Wang, Z. LLM‑EnhancedFramework for Building Domain‑Specific Lexicon for Urban PowerGrid Design. Appl. Sci. 2025, 15, 4134.https://doi.org/10.3390/app15084134Copyright: © 2025 by the authors.LicenseeMDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).ArticleLLM‑Enhanced Framework for Building Domain‑SpecificLexicon for Urban Power Grid DesignYan Xu 1, TaoWang 1, Yang Yuan 1, Ziyue Huang 1, Xi Chen 2,* , Bo Zhang 3 , Xiaorong Zhang 2 and ZehuaWang 41 State Grid Jiangsu Electric Power Design Consulting Co., Ltd., Xuzhou Survey and Design BranchCompany, Xuzhou 221000, China; 17805499284@163.com (Y.X.); 18606172522@163.com (T.W.);19851625313@aliyun.com (Y.Y.); 17358626339@aliyun.com (Z.H.)2 School of Mechanical and Civil Engineering, China University of Mining and Technology,Xuzhou 221000, China; ts22030290a31tm@cumt.edu.cn3 School of Computer Science & Technology, China University of Mining and Technology,Xuzhou 221000, China; zbcumt@163.com4 Department of Electrical and Computer Engineering, The University of British Columbia,Vancouver, BC V6T 1Z4, Canada; zwang@ece.ubc.ca* Correspondence: x.chen@cumt.edu.cnAbstract: Traditional methods for urban power grid design have struggled to meet thedemands of multi‑energy integration and high resilience scenarios due to issues such asdelayedupdates of terminology and semantic ambiguity. Current techniques for construct‑ing domain‑specific lexicons face challenges like the insufficient coverage of specializedvocabulary and imprecise synonym mining, which restrict the semantic parsing capabil‑ities of intelligent design systems. To address these challenges, this study proposes aframework for constructing a domain‑specific lexicon for urban power grid design basedon Large Language Models (LLMs). The aim is to enhance the accuracy and practical‑ity of the lexicon through multi‑level term extraction and synonym expansion. Initially,a structured corpus covering national and industry standards in the field of power wasconstructed. An improved Term Frequency–Inverse Document Frequency (TF‑IDF) algo‑rithm, combined with mutual information and adjacency entropy filtering mechanisms,was utilized to extract high‑quality seed vocabulary from 3426 candidate terms. Leverag‑ing LLMs, multi‑level prompt templates were designed to guide synonym mining, incor‑porating a self‑correctionmechanism for semantic verification to mitigate errors caused bymodel hallucinations. This approach successfully built a domain‑specific lexicon compris‑ing 3426 core seed words and 10,745 synonyms. The average cosine similarity of synonympairs reached 0.86, and expert validation confirmed an accuracy rate of 89.3%; text classifi‑cation experiments showed that integrating the domain‑specific dictionary improved theclassifier’s F1‑score by 9.2%, demonstrating the effectiveness of the method. This researchinnovatively constructs a high‑precision terminology dictionary in the field of power de‑sign for the first time through embedding domain‑driven constraints and validation work‑flows, solving the problems of insufficient coverage and imprecise expansion of traditionalmethods, and supporting the development of semantically intelligent systems for smart ur‑ban power grid design, with significant practical application value.Keywords: electric power grid design; domain‑specific lexicon; synonym mining; largelanguage models; natural language processingAppl. Sci. 2025, 15, 4134 https://doi.org/10.3390/app15084134Appl. Sci. 2025, 15, 4134 2 of 161. IntroductionTraditional methods for power grid design lack dynamic knowledge‑updating mech‑anisms, making it challenging to adapt to business scenarios requiring multi‑energy in‑tegration and high resilience. Meanwhile, the deep integration of big data in power sys‑tems with artificial intelligence technologies offers new perspectives for industry innova‑tion. However, the effective utilization of power domain knowledge still faces critical bot‑tlenecks: existing industry terminology standards struggle to cover all domain‑specificvocabulary, including colloquial terms, alternative names, and abbreviations, along withissues like ambiguous definitions and delayed updates. These challenges significantly re‑strict the semantic parsing capabilities of intelligent design systems [1]. In this context, asthe semantic cornerstone of knowledge‑intensive systems, the quality of domain‑specificlexicons directly impacts the depth of understanding by intelligent systems of professionalknowledge. Particularly in the power sector, a critical national infrastructure domain, con‑structing highly accurate domain‑specific lexicons has become a core requirement for sup‑porting the intelligent transformation of power grids.Domain‑specific lexicons serve as semantic analysis tools applicable to specific do‑mains, compiling and distilling vocabulary from domain‑related corpora to convey charac‑teristic information of the field. The development of domain‑specific lexicon constructiontechniques has undergone three primary stages [2]: manual methods, statistical methods,and deep learning‑based methods. Early‑stage lexicon construction mainly relied on ex‑pert knowledge and manually compiling entries, which typically resulted in high‑qualitylexicons but at the cost of significant time and human resources. Statistical methods lever‑aged large‑scale corpora by automatically extracting terminology through calculating co‑occurrence frequencies and other statistical features betweenwords. However, thesemeth‑ods heavily depend on high‑quality corpora and strugglewith handling complex linguisticphenomena such as polysemy and collocations. Deep learning methods utilize neural net‑works to automatically learnword representations and incorporate contextual informationfor term recognition and classification. Nonetheless, this approach requires vast amountsof annotated data for model training and high computational resource demands, and itsuffers from poor model interpretability. Despite these challenges, deep learning methodshave 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, suchas 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 wordstrings that do not meet the criteria are filtered out to obtain seed vocabulary. (3) Syn‑onym Mining: by calculating word vector similarities, synonyms of seed vocabulary arediscovered to expand the domain‑specific lexicon.As the foundational component of a domain‑specific lexicon, the quality of seed vo‑cabulary directly impacts the effectiveness and application scope of the final lexicon. Itsconstruction methods have evolved through three paradigms:(1) Rule‑basedMethods: Early studies utilized manually defined regular expressions,grammar rules, or statistical thresholds to screen candidate terms, such as the TF‑IDFweighted model [7], which selects vocabulary based on the distribution characteristics ofterms within document sets. While these methods offer interpretability advantages, theyrely heavily on manually designed feature rules, making it challenging to adapt to vastand complex semantic scenarios.Appl. Sci. 2025, 15, 4134 3 of 16(2) Statistical Learning Methods: Represented by N‑gram models [8] and mutual in‑formation combined with adjacency entropy methods [9], these approaches capture co‑occurrence patterns of vocabulary using probabilistic models. For instance, Shen et al. [10]achieved a new termdiscovery accuracy rate of 76% by combining Jieba segmentationwithstatistical filtering for seed vocabulary extraction.(3) Deep Learning Methods: Utilizing Word2Vec [11], BERT [12], and other embed‑ding techniques, these methods capture semantic associations through distributed repre‑sentations, enabling the identification of semantically similar words. However, deep mod‑els require large‑scale annotated data, which conflicts with the scarcity of power domaincorpora.Synonym expansion not only enriches the content of domain‑specific lexicons but alsoenhances themodel’s understanding of complex semantic relationshipswithin the domain.Synonym mining can be divided into two directions: synonym differentiation and syn‑onym set extraction.Synonym differentiation aims to determine whether two given words are synonyms.Early research, such as Ramage et al.’s work [13], often relied on manually constructedlexical resources to query these resources and judge synonymy between words. Mikolovet al.’s word2vec model [14] maps words into a continuous vector space, where semanti‑cally similar words are closer. This method not only improves efficiency but also identifiessynonyms without pre‑defined vocabularies. Additionally, some studies combine knowl‑edge graphs with deep learning methods; for example, Guo et al. [15] proposed a neuralnetwork model incorporating knowledge graph information, significantly improving theaccuracy of synonym differentiation.Synonym set extraction involves automatically discovering new synonym combina‑tions from large corpora. Traditional approaches utilize co‑occurrence analysis [16], calcu‑lating the frequency at which words appear together in the same context as a measure ofsynonymy. Hamilton et al. [17] developed a Graph Neural Network (GNN)‑based algo‑rithm to extract synonym sets from text data, effectively capturing deeper semantic con‑nections between words by modeling complex relationships. Some studies also exploreunsupervised or semi‑supervised learning strategies for synonym set mining [18] to re‑duce reliance on annotated data.In summary, although existing methods have achieved certain successes in construct‑ing lexicons for general or some vertical domains, the power design domain exhibits sig‑nificant uniqueness:(1) The terminology system in this domain is complex, encompassing both specializedcompound terms like “tower load” and “insulation coordination”, as well as cross‑domainhigh‑frequency terms like “grounding” and “protection”. Traditional statistical modelsare susceptible to issues like corpus sparsity and semantic ambiguity.(2) Current synonym mining techniques lack targeted solutions for mapping syn‑onyms 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 designdomain?(2) How can one effectively handle complex linguistic phenomena, such as polysemyand collocations, in the power design domain based on the vocabulary set, thereby expand‑ing 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), asillustrated in Figure 1.Appl. Sci. 2025, 15, 4134 4 of 16Appl. Sci. 2025, 15, x FOR PEER REVIEW 4 of 16 Figure 1. Conceptual figure of this study. 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–correc-tion”. In the statistical stage, improved traditional mutual information algorithms are used, and domain coefficients defined by power industry standards are introduced to en-sure the recall rate of core vocabulary. In the expansion phase, a power-specific prompt template was designed to constrain the LLM to generate synonyms that are suitable for engineering scenarios. In the correction phase, an innovative self-correction mechanism was integrated with the LLM to reduce the error rate of synonyms through secondary verification. Meanwhile, the static lexicon construction method will lead to an increase in the cost of updating and maintaining the dictionary. This study supports the real-time expansion of new terms through structured corpora and incremental prompt templates to meet the rapid iteration requirements of power grid design standards. The core hypothesis of this research is that LLM-driven term extraction and synonym expansion can significantly improve both the accuracy and coverage of power design lex-icons, overcoming limitations of traditional methods in handling domain-specific seman-tic ambiguity and terminology sparsity. 2. Materials and Methods The framework of this study is divided into three stages: data preprocessing, seed vocabulary extraction, and synonym discovery, as illustrated in Figure 2. Figure 2. Framework for building a dictionary in the field of power design. In the data preprocessing stage, we first define the scope of our corpus and perform the slicing and recombination of standard entries to create a structured corpus suitable for subsequent processing needs. In the seed vocabulary extraction stage, we utilize the Jieba segmentation tool for word segmentation and combine mutual information and entropy-based methods to filter out non-specialized vocabulary, thereby establishing a multi-level candidate term filtering system that significantly enhances the accuracy of seed vocabu-lary extraction. Finally, in the synonym discovery stage, we leverage the semantic under-standing capabilities of Large Language Models (LLMs) to conduct synonym mining within the power domain, further enriching the lexicon content and expanding its scale. Figure 1. ce t al fi re of this study.The novelty of this research is reflected in the following:nlike existing studies that often use statistical ethods or LL s independently, thisstudy proposes a three‑level collaborative architecture of “statistics–extension–c rrection”.In the statistical stage, improved traditional mutual information algorithms are used, anddomain coefficients defined by power industry standards are introduced to ensure the re‑call rate of core vocabulary. In the expansion phase, a power‑specific prompt templatewas designed to constrain the LLM to generate synonyms that are suitable for engineeringscenarios. In the correction phase, an innovative self‑correctionmechanismwas integratedwith the LLM to reduce the error rate of synonyms through secondary verification. Mean‑while, the static lexicon constructionmethodwill lead to an increase in the cost of updatingandmaintaining the dictionary. This study supports the real‑time expansion of new termsthrough structured corpora and incremental prompt templates to meet the rapid iterationrequirements of power grid design standards.The core hypothesis of this research is that LLM‑driven term extraction and synonymexpansion can significantly improve both the accuracy and coverage of power design lexi‑cons, overcoming limitations of traditional methods in handling domain‑specific semanticambiguity and terminology sparsity.2. Materials and MethodsThe framework of this study is divided into three stages: data preprocessing, seedvocabulary extraction, and synonym discovery, as illustrated in Figure 2.Appl. Sci. 2025, 15, x FOR PEER REVIEW 4 of 16 Figure 1. Conceptual figure of this study. 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–correc-tion”. In the statistical stage, improved traditional mutual information algorithms are used, and domain coefficients defined by power industry standards are introduced to en-sure the recall rate of core vocabulary. In the expansion phase, a power-specific prompt template was designed to constrain the LLM to generate synonyms that are suitable for engineering scenarios. In the correction phase, an innovative self-correction mechanism was integrated with the LLM to reduce the error rate of synonyms through secondary verification. Meanwhile, the static lexicon construction method will lead to an increase in the cost of updating and maintaining the dictionary. This study supports the real-time expansion of new terms through structured corpora and incremental prompt templates to meet the rapid iteration requirements of power grid design standards. The core hypothesis of this research is that LLM-driven term extraction and synonym expansion can significantly improve both the accuracy and coverage of power design lex-icons, overcoming limitations of traditional methods in handling domain-specific seman-tic ambiguity and terminology sparsity. . t ri ls t s f f t i t i i i i t t t : t i , l t ti , i , ill t t i i . Figure 2. Framework for building a dictionary in the field of power design. In the data preprocessing stage, we first define the scope of our corpus and perform the slicing and recombination of standard entries to create a structured corpus suitable for subsequent processing needs. In the seed vocabulary extraction stage, we utilize the Jieba segmentation tool for word segmentation and combine mutual information and entropy-based methods to filter out non-specialized vocabulary, thereby establishing a multi-level candidate term filtering system that significantly enhances the accuracy of seed vocabu-lary extraction. Finally, in the synonym discovery stage, we leverage the semantic under-standing capabilities of Large Language Models (LLMs) to conduct synonym mining within the power domain, further enriching the lexicon content and expanding its scale. Figure 2. Framework for building a dictionary in the field of power design.t t i t , fi t fi tli i i i i i l. I t s l ,s t ti c i e t al infor atio and entropy‑‑ ‑filtering system that significantly enhances the accuracy of seed vocabularyextraction. Fi ally, in the synonym discovery stage, we lev rage the semantic understand‑ing capabilities of Large Lan uage Models (LLMs) to conduct synonymmi i g withi thepower domain, further e riching the lexicon content a d expandi g its scale.Appl. Sci. 2025, 15, 4134 5 of 162.1. Corpus Determination and Data PreprocessingCorpus construction strictly adheres to the principles of scientific rigor, representa‑tiveness, and practicality. The primary sources are national and industry standards, specif‑ically including the following:1. Core Standard Set: This includes 17 current standards, such as GB/T 50934‑2013 [19]“Design Code for Power Engineering”, DL/T 5218‑2012 [20] “Technical RegulationsforDesign of 220 kV to 750 kV Substations”, andDL/T 1033‑2016 [21] “Power IndustryVocabulary”.2. Extended Standard Set: This includes 6 enterprise standards, such as Q/GDW 11184‑2014 “Technical Guidelines for Distribution Network Planning and Design.”In addition, to enhance the practicality and coverage of the corpus, various types ofdesign documents, such as preliminary design reports and construction drawing designexplanations, as well as actual engineering cases, have been included.During the data preprocessing stage, we employ an automated text processingmethod based on regular expressions to split the original text into entries, extracting clause‑level semantic units. The matching expression used isAppl. Sci. 2025, 15, x FOR PEER REVIEW 5 of 16 2.1. Corpus Determination and Data Preprocessing Corpus construction strictly adheres to the principles of scientific rigor, representa-tiveness, and practicality. The primary sources are national and industry standards, spe-cifically including the following: 1. Core Standard Set: This includes 17 current standards, such as GB/ 50934-2013 [19] “Design Code for Power Engineering”, DL/T 5218-2012 [20] “Technical Regulations for Design of 220 kV to 750 kV Substations”, and DL/T 1033-2016 [21] “Power Indus-try Vocabulary”. 2. Extended Standard Set: This includes 6 enterprise standards, such as Q/GDW 11184-2014 “Technical Guidelines for Distribution Network Planning and Design.” In addition, to enhance the practicality and coverage of the corpus, various types of design documents, such as preliminary design reports and construction drawing design explanations, as well as actual engineering cases, have been included. During the data preprocessing stage, we employ an automated text processing method based on regular expressions to split the original text into entries, extracting clause-level semantic units. The matching expression used is 𝑝𝑎𝑡𝑡𝑒𝑟𝑛 = r′第([\d. ]+)条\s + (. +? )(? = 第[\d. ] + 条|$)′ This regex is designed to match Chinese legal/article clauses format, e.g., "第 1 条", "第 1.2 条".It captures the clause number and its content until the next clause or the end of the document. This process converts the original PDF documents into a structured JSON format, where each data unit contains four fields: standard number, clause number, technical con-tent, and associated figure/table index. This provides a solid data foundation for subse-quent text mining and analysis. To ensure the quality of the corpus, we have established a comprehensive set of cleaning rules specific to the power domain, which include the following: 1. Removal of Non-technical Descriptions: Phrases like “This article explains as fol-lows”: are deleted. 2. Standardization of Mathematical Symbols: For example, converting “≥” to “>=” and correcting “KVar” to “kvar”. 3. Normalization of Equipment Models: For example, standardizing “KYN28A-12” to “KYN28-12”. 2.2. Candidate Term Extraction In the seed vocabulary extraction stage, this study constructs a three-level stop word list tailored to the characteristics of the corpus, as shown in Table 1. Table 1. List of stop words. Category Word Examples Count Filtering Criteria General Stop Words 的, 是, 在 1892 Function Words in Chinese Grammar Domain-specific Stop Words 相关, 规定要求 437 High-frequency Non-indicative Terms in Tech-nical Standards Numeric Stop Words 1.2.3, ①, 附录A 215 Non-semantic Numeric Symbols Based on this list, we employ a bidirectional filtering strategy to optimize the vocab-ulary extraction process. The forward elimination strategy directly matches terms against the stop word lists to remove irrelevant entries, while the reverse retention strategy uses a whitelist to protect engineering-specific symbols such as “N-1” and “kA”, ensuring that critical terminology is not mistakenly deleted. This regex is designed to match Chinese legal/article clauses format, e.g., “第1条”,“第1.2条”. It captures the clause number and its content until the next clause or the end ofthe document.This process converts the original PDF documents into a structured JSON format,where each data unit contains four fields: standard number, clause number, technical con‑tent, and associated figure/table index. This provides a solid data foundation for subse‑quent text mining and analysis.To ensure the quality of the corpus, we ave establis ed a comprehensive set of clean‑ing rules specific to the power domain, which include the following:1. Removal of Non‑technical Descriptions: Phrases like “This article explains as fol‑lows”: are deleted.2. Standardization of Mathematical Symbols: For example, converting “≥” to “>=” andcorrecting “KVar” to “kvar”.3. Normalization of Equipment Models: For example, standardizing “KYN28A‑12” to“KYN28‑12”.2.2. Candidate Term ExtractionIn the seed vocabulary extraction stage, this study constructs a three‑level stop wordlist tailored to the characteristics of the corpus, as shown in Table 1.Table 1. List of stop words.Category Word Examples Count Filtering CriteriaGeneral StopWords 的,是,在 1892Function Words inChinese GrammarDomain‑specificStop W rds 相关,规定要求 437High‑frequencyNon‑indicativeTerms in TechnicalStandardsNumeric StopWords 1.2.3, ¬,附录A 215Non‑semanticNumeric SymbolsAppl. Sci. 2025, 15, 4134 6 of 16Based on this list, we employ a bidirectional filtering strategy to optimize the vocab‑ulary extraction process. The forward elimination strategy directly matches terms againstthe stop word lists to remove irrelevant entries, while the reverse retention strategy usesa whitelist to protect engineering‑specific symbols such as “N‑1” and “kA”, ensuring thatcritical terminology is not mistakenly deleted.The frequency of a term’s appearance in the corpus is indicative of its likelihood of be‑ing a high‑quality professional term. To extract seed vocabulary, we use the Jieba segmen‑tation tool for word segmentation and combine it with an improved TF‑IDF algorithm [22]for term frequency feature analysis. The weight calculation model is given by Formula 1:W(t) = N· f (t)× log(d(t) + 1D)× Cdomain(t) (1)wheref (t): The number of occurrences of term t in the corpus.N: Total number of words in the corpus.D: Total number of standard documents.d(t): Number of documents containing term t.Cdomain(t): Domain coefficient. Terms appearing in DL/T 1033‑2016 “Power IndustryVocabulary” are assigned a coefficient of 1.5, while marginal terms receive a coefficientof 1.0.By setting tiered screening thresholds, the extracted terms are classified into threecategories based on their weights: high‑frequency terms (W(t) ≥ 0.85 × max(W)), mid‑frequency terms (0.6 × max(W) ≤ W(t) < 0.85 × max(W)), and low‑frequency terms(W(t) < 0.6 × max(W)). Only high‑frequency terms are selected for the candidate term fil‑tering stage.2.3. Candidate Term FilteringTo further enhance the quality of the seed vocabulary, this study proposes a filter‑ing method based on mutual information and entropy for domain‑specific professionalvocabulary. This approach effectively improves the accuracy of domain term recognitionby considering both the internal cohesion and external independence of vocabulary. Theprocess involves three key steps:1. Preprocessing and Candidate Vocabulary Generation: Perform sentence segmenta‑tion andword segmentation on the corpus text to generate high‑frequency word segmentsas the candidate vocabulary set.2. Mutual Information and Entropy Calculation: Calculate the mutual informationand entropy for all candidate terms. The weighted sum of these two statistical metrics isused as the final scoring criterion.3. Sorting and Manual Selection: Rank the terms based on their scores and combinemanual selection to obtain a high‑quality set of domain‑specific seed words.Mutual information (MI) [23] is a crucial statistic for measuring the internal cohesionofwordpairs, which is particularly advantageous inChineseword segmentation problems.It is defined by Formula (2):MI(x, y) = log2p(x, y)p(x)p(y)(2)wherep(x,y): Joint probability of co‑occurrence of two words.p(x) and p(y): Marginal probabilities of each word occurring independently.Appl. Sci. 2025, 15, 4134 7 of 16In practical applications, a higher MI value indicates a stronger bond between twocharacters, suggesting a higher likelihood of forming a valid word. This method is espe‑cially effective for identifying domain‑specific terms and fixed expressions, as professionalterminology often exhibits high internal cohesion.Adjacent entropy [24] measures the external independence of a word string by cal‑culating the uncertainty of adjacent characters on either side of the candidate term. Leftadjacent entropy (HL) and right adjacent entropy (HR) are calculated using Formulas (3)and (4), respectively:Hle f t(W) = − ∑WL∈SLP(WL|W)log2P(WL|W) (3)Hright(W) = − ∑WR∈SRP(WR|W)log2P(WR|W) (4)whereSL and SR: Sets of left and right adjacent characters of the candidate termW.P(WL|W) and P(WR|W): Conditional probabilities of specific characters being left orright adjacent characters ofW.P(WL|W) = N(WL,W)N(W) (5)P(WR|W) = N(WR,W)N(W) (6)whereN(WL,W) andN(WR,W): Counts of left and right adjacent characters co‑occurringwiththe candidate termW.N(W): Total number of occurrences of the candidate termW.By integrating mutual information and adjacent entropy, we establish a dual‑filteringmechanism to extract core seed vocabulary relevant to urban power grid design. Thismethod not only evaluates the internal cohesion strength of vocabulary but also assessesits independence within contextual environments, significantly enhancing the accuracy ofterm recognition.The final step involves ranking the candidate terms based on their combined scoresfrom mutual information and adjacent entropy. High‑scoring terms are then manually re‑viewed to ensure their relevance and accuracy before inclusion in the final seed vocabularyset. This refined set serves as the foundation for subsequent synonym mining.This comprehensive approach ensures that the resulting seed vocabulary is both pre‑cise and contextually appropriate, providing a robust basis for building an extensive andaccurate domain‑specific lexicon for the power design field. The filtered seed vocabularyset will be used in the next stage for synonym discovery, thereby enriching the lexicon andenhancing its utility for intelligent systems in urban power grid design.2.4. Synonym Mining AlgorithmLarge Language Models (LLMs) are a significant achievement in the field of naturallanguage processing, capable of understanding and generating high‑quality natural lan‑guage text. However, directly using LLMs to obtain domain‑specific synonyms carriescertain risks, such as model hallucinations, where the model may generate seemingly rea‑sonable but actually inaccurate or irrelevant terms. To address the aforementioned issues,Prompt Engineering has been proposed as an effective strategy [25]. We propose a algo‑Appl. Sci. 2025, 15, 4134 8 of 16rithm (see Algorithm 1) which can improve the quality of the output and enhance thecontrollability and accuracy of the results.For each seed vocabulary, we set up a synonym mining prompt to guide the LLMto output new terms that are semantically equivalent to the seed vocabulary. By design‑ing multi‑level prompt templates, we can guide the LLM to perform semantic reasoningfor terminology. To avoid errors caused by model hallucinations, we also design a self‑correction mechanism to ensure semantic consistency through secondary verification. Thestructured prompt design in this study includes the following components:Prompt = Pre f ix+ Instruct+ Examples+ Input (7)wherePrefix: A fixed module setting for the model.Instruct: Detailed instructions on the specific task requirements, including task de‑scriptions and desired output formats.Examples: Specific examples of the task to control the LLM’s responses.Input: The text to be extracted.The extraction process primarily involves two prompts:(1) SynonymMining PromptThis template is designed to guide the LLM to find synonyms for the given seed vocab‑ulary. Considering that some terms may not have synonyms, the Input allows the modelto output “无” (none) when no synonyms are found. This module uses examples such as“多元件绝缘子” (multi‑element insulator) and “复合绝缘元件” (composite insulation com‑ponent) to guide the model in recognizing synonym relationships between vocabulary.(2) Self‑Correction Mechanism PromptThis module is motivated by the potential for errors or inconsistencies in the gener‑ated outputs from the LLM. We propose a self‑correction strategy to encourage the modelto self‑verify its generated synonyms, thereby reducing errors. The Instruct in the self‑correction mechanism prompt guides the LLM to read the generated vocabulary and com‑pare it semantically with the seed vocabulary. If the generated term is consistent with theseed vocabulary, the model outputs “Yes”; otherwise, it deletes the inconsistent term andoutputs the remaining valid terms.The processing flow of the algorithm is shown in Figure 3:Appl. Sci. 2025, 15, x FOR PEER REVIEW 8 of 16 where Prefix: A fixed module setting for the model. Instruct: Detailed instructions on the specific task requirements, including task de-scriptions and desired output formats. Examples: Specific examples of the task to control the LL ’s responses. Input: The text to be extracted. The extraction process primarily involves two prompts: (1) Synony ining Pro pt This te plate is designed to guide the LL to find synonyms for the given seed vo-cabulary. Considering that some ter s may not have synonyms, the Input allows the model to output “无” (none) when no synonyms are found. This module uses examples such as “多元件绝缘子” (multi-element insulator) and “复合绝缘元件” (composite insula-tion component) to guide the model in recog izing synonym relationships between vo-cabulary. (2) Self-Correction Mechanism Prompt This module is motivated by the potential f r errors or inconsistencies in the gener-ated outputs from the LLM (Figure 3). We propos a self-correction strategy to e courage the model to self-verify its generated synonyms, thereby reducing errors. The Instruct in the self-correction mechanism prompt g ides the LLM to rea the generated vocabulary and compare it semantically with the se d v cabulary. If the generated term is consiste t with the se d vocabulary, the model outputs “Yes”; otherwise, it deletes the inconsistent term and output the remaining valid terms. Figure 3. Algorithm process of synonym mining. The various parts of the prompt are shown in Table 2: Table 2. Template for synonym mining and self-correction prompt. Prompt Module Content Prefix You are an experienced expert in the field of power design. Instruct_synonym_mining Task: generate strictly synonymous terms based on the given vocabulary, and output ‘none’ if none are available. Format: comma-separated list. Examples_synonym_mining Input: multi-component insulator ->Output: Composite insulation component. Instruct_self_correction Task: determine if ‘{term}’ is strictly synonymous with ‘{seed_word}’. Format: answer ‘yes’ or‘ no ‘. Examples_self_correction Seed_word: multi-component insulator Term: composite insulating element -> Yes. The pseudo-code for the algorithm is: Algorithm 1 Synonym mining enhanced by LLM. Input: Seed vocabulary Figure 3. Algorithm process of synonym mining.The various parts of the pro pt are sho n i l :Appl. Sci. 2025, 15, 4134 9 of 16Table 2. Template for synonym mining and self‑correction prompt.Prompt Module ContentPrefix You are an experienced expert in the field of power design.Instruct_synonym_miningTask: generate strictly synonymous terms based on the givenvocabulary, and output ‘none’ if none are available.Format: comma‑separated list.Examples_synonym_mining Input: multi‑component insulator ‑>Output: Composite insulationcomponent.Instruct_self_correctionTask: determine if ‘{term}’ is strictly synonymous with‘{seed_word}’.Format: answer ‘yes’ or‘ no ‘.Examples_self_correction Seed_word: multi‑component insulator Term: compositeinsulating element ‑> Yes.The pseudo‑code for the algorithm is:Algorithm 1 Synonym mining enhanced by LLM.Input: Seed vocabularyOutput: Collection of synonyms for this vocabulary1. function synonym_mining(seed_word)2. Prompt = Prompt: Synonym mining3. output_word_list = llm(Prompt)4. if output_word_list is empty then5. output_word_list = [“None”]6. return output_word_list7. end function8. function self_correction(word_list, seed_word)9. verified_words = []10. foreach word in word_list do11. Prompt = Prompt: Self correction12. verification_result = llm(Prompt)13. if verification_result == “Yes” then14. verified_words.append(word)15. end if16. end foreach17. return verified_words18. end function3. Results3.1. Seed Vocabulary MiningIn this section, the experimental data were derived from national and industry designcodes and standards. Given that the raw corpus contains a large number of stop words,conjunctions, and adjectives, we used the Chinese Jieba segmentation tool to tokenize thetext and obtain corresponding part‑of‑speech tags. During seed vocabulary mining, weextracted a total of 5623 domain‑specific phrases. After filtering through mutual infor‑mation and entropy‑based methods, 2196 invalid phrases such as “验收情况” (acceptancesituation), “按照国家” (according to the country), and “值及以上” (value and above) werefiltered out. The remaining 3426 phrases were used as seed vocabulary for synonym min‑ing, forming an initial domain lexicon with 3426 synonyms.Appl. Sci. 2025, 15, 4134 10 of 163.2. Parameter SettingsWe utilized the Qwen‑Max Large Language Model (LLM) API for our experiments.The relevant parameters are summarized in Table 3:Table 3. Main parameter settings of the Large Language Model.Parameter Description Valuetop_pThe probability threshold for controlling the kernel samplingmethod ranges from 0 to 1, with higher values indicatinghigher randomness.0.8temperatureControl the randomness and diversity of the generated content,with a range between 0 and 2. The larger the value, the higherthe flexibility of the output content.0.1max_tokens Used to limit the number of tokens generated by the model. 2000In this case, the selection of parameters aims to optimize the accuracy and efficiencyof synonym mining. We chose a top_p‑value of 0.8 because it can reduce irrelevant orlow‑quality output while ensuring the diversity of generated text, ensuring that the modelcan explore multiple possible terminology expressions while maintaining a certain level ofaccuracy. In order to further improve the accuracy of synonymmatching, a lower temper‑ature value (0.1) was selected to reduce the uncertainty generated by the model and makethe outputmore inclined towards high‑probabilitywords, thereby enhancing the relevanceand accuracy of the results. Considering that the response requested in this study is rela‑tively simple, involving only synonymous entities or self‑correction results, and that thereare no special requirements for max_token, the model default value of 2000 was adoptedto ensure sufficient generation space.3.3. Results of Synonym Mining and Self‑CorrectionThe synonymmining process began with a 3425‑seed vocabulary. After applying thesynonym mining algorithm enhanced by LLMs, the vocabulary was expanded to include11,265 terms, with Table 4 showcasing some typical examples from the synonym mining.Table 4. Synonym mining results (partial typical cases).SeedVocabulary Synonyms 1 Synonyms 2 Synonyms 3高压绝缘子(high‑voltage insulator)高压瓷瓶(high‑voltage porcelaininsulator)高压绝缘子串(high‑voltage insulatorstring)高压绝缘子(high‑voltage ceramicinsulator)星型接线(star‑shaped wiring)星形联结(star connection)Y连接(Y connection)Y型接法(Y‑shaped connection)馈线断路器(feeder circuit breaker)馈线开关(feeder switch)馈线保护开关(feeder protection switch)馈线开关设备(feeder switch equipment)Following the synonym mining phase, the self‑correction stage commenced, duringwhich unsuitable synonyms were removed, reducing the total number from 11,265 to10,745. The corrected synonym results are displayed in Figure 3, with each seed term aver‑aging 2–3 synonyms mined. A frequency distribution histogram is provided in Figure 4.Appl. Sci. 2025, 15, 4134 11 of 16Appl. Sci. 2025, 15, x FOR PEER REVIEW 10 of 16 relevance and accuracy of the results. Considering that the response requested in this study is relatively simple, involving only synonymous entities or self-correction results, and that there are no special requirements for max_token, the model default value of 2000 was adopted to ensure sufficient generation space. 3.3. Results of Synonym Mining and Self-Correction The synonym mining process began with a 3425-seed vocabulary. After applying the synonym mining algorithm enhanced by LLMs, the vocabulary was expanded to include 11,265 terms, with Table 4 showcasing some typical examples from the synonym mining. Table 4. Synonym mining results (partial typical cases). Seed Vocabulary Synonyms 1 Synonyms 2 Synonyms 3 高压绝缘子 (high-voltage insulator) 高压瓷瓶 (high-voltage porcelain insu-lator) 高压绝缘子串 (high-voltage insulator string) 高压绝缘子 (high-voltage ceramic insula-tor) 星型接线 (star-shaped wiring) 星形联结 (star connection) Y连接 (Y connection) Y型接法 (Y-shaped connection) 馈线断路器 (feeder circuit breaker) 馈线开关 (feeder switch) 馈线保护开关 (feeder protection switch) 馈线开关设备 (feeder switch equipment) Following the synonym mining phase, the self-correction stage commenced, during which unsuitable synonyms were removed, reducing the total number from 11,265 to 10,745. The corrected synonym results are displayed in Figure 3, with each seed term av-eraging 2–3 synonyms mined. A frequency distribution histogram is provided in Figure 4. Figure 4. Histogram of synonym count after self-correction. A typical example is “直接接触” (direct contact). During synonym mining, the model suggested synonyms like “密切接触” (close contact), “表面接触” (surface contact), and “直接接触传播” (direct contact transmission). However, in the context of power design, “直接接触” specifically refers to direct contact with live parts leading to electric shock. Thus, during the self-correction process, semantically irrelevant synonyms were removed. Figure 4. Histogram of synonym count after self‑co rection.A typical exa ple is “ ” (direct contact). During synonymmining, the modelsuggested synonyms like “密切接触” (close contact), “表面接触” (surface contact), and“直接接触传播” (direct contact transmission). However, in the context of power design,“直接接触” specifically refers to direct contact with live parts leading to electric shock.Thus, during the self‑correction process, semantically irrelevant synonyms were removed.3.4. Semantic Accuracy EvaluationTo ensure the semantic accuracy of the synonym mining results, this study employsa combination of cosine similarity calculations based on word embeddings and expert val‑idation. This dual approach evaluates the generated synonyms from both automated as‑sessment and domain knowledge perspectives.(1) Cosine Similarity Analysis Based on Word EmbeddingsWe use a domain‑fine‑tuned pre‑trained BERT model to vectorize the 10,745 pairs ofsynonyms (seed vocabulary and their synonyms) obtained after self‑correction. The cosinesimilarity between each pair is calculated using the following formula:CosineSimilarity =A · B‖A‖‖B‖ (8)whereA·B: Dot product of the vectors for terms A and B.‖A‖ and ‖B‖: Magnitudes (norms) of the vectors for terms A and B.The resulting cosine similarity histogram for the synonym pairs is shown in Figure 5.The results show that the average cosine similarity of all word pairs is 0.86 (standarddeviation = 0.12), and the proportion of similarity ≥ 0.75 is 84.3%, indicating that syn‑onyms are highly consistent in the semantic space. For example, “绝缘子” (insulator) and“复合绝缘元件” (composite insulation component) have a similarity score of 0.91. In con‑trast, the incorrect pairing of “直接接触” (direct contact) and “表面接触” (surface contact)has a similarity score of only 0.32, consistent with the manual correction results.(2) Expert ValidationThree experts with over 10 years of experience in power design were invited to val‑idate a random sample of 100 synonym pairs. The criteria for acceptance were that thesynonyms should be interchangeable in engineering contexts without introducing seman‑tic ambiguity.The result is shown in Table 4. From a random sample of 100 synonym pairs, thethree experts collectively validated 268 pairs, achieving an expert validation accuracy rateAppl. Sci. 2025, 15, 4134 12 of 16of 89.3%. This indicates the effectiveness of our method in generating accurate and contex‑tually appropriate synonyms.Appl. Sci. 2025, 15, x FOR PEER REVIEW 11 of 16 3.4. Semantic Accuracy Evaluation To ensure the semantic accuracy of the synonym mining results, this study employs a combination of cosine similarity calculations based on word embeddings and expert validation. This dual approach evaluates the generated synonyms from both automated assessment and domain knowledge perspectives. (1) Cosine Similarity Analysis Based on Word Embeddings We use a domain-fine-tuned pre-trained BERT model to vectorize the 10,745 pairs of synonyms (seed vocabulary and their synonyms) obtained after self-correction. The cosine similarity between each pair is calculated using the following formula: 𝐶𝑜𝑠𝑖𝑛𝑒𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 = 𝐴 ⋅ 𝐵∥ 𝐴 ∥∥ 𝐵 ∥ (8)where A⋅B: Dot product of the vectors for terms A and B. ∥A∥ and ∥B∥: Magnitudes (norms) of the vectors for terms A and B. The resulti g cosine similarity histogram for t e sy onym pairs is shown in Figure 5. Figure 5. Histogram of Semantic Cosine Similarity. The results show that the average cosine similarity of all word pairs is 0.86 (standard deviation = 0.12), and the proportion of similarity ≥ 0.75 is 84.3%, indicating that synonyms are highly consistent in the semantic space. For example, “绝缘子” (insulator) and “复合绝缘元件” (composite insulation component) have a similarity score of 0.91. In contrast, the incorrect pairing of “直接接触” (direct contact) and “表面接触” (surface contact) has a similarity score of only 0.32, consistent with the manual correction results. (2) Expert Validation Three experts with over 10 years of experience in power design were invited to vali-date a random sample of 100 synonym pairs. The criteria for acceptance were that the synonyms should be interchangeable in engineering contexts without introducing seman-tic ambiguity. The result is shown in Table 4. From a random sample of 100 synonym pairs, the three experts collectively validated 268 pairs, achieving an expert validation accuracy rate of 89.3%. This indicates the effectiveness of our method in generating accurate and con-textually appropriate synonyms. 3.5. Analysis of Dictionary Effectiveness and Sensitivity Figure 5. Histogram of Semantic Cosine Similarity.3.5. Analysis of Dictionary Effectiveness and SensitivityTo validate the effectiveness of the domain‑specific lexicon built in this study, weevaluated the quality of the dictionary based on the accuracy of domain document recog‑nition. The experimental dataset used in this section consists of 1000 corpus entries relatedto power design (independent of the corpus described in Section 2.1). The evaluation wasconducted by comparing the performance of a Support Vector Machine (SVM)model withand without loading the domain dictionary into the custom dictionary of the Jieba tok‑enizer. The specific experimental steps are as follows:(1) The domain dictionary was loaded into the custom dictionary of Jieba for the ex‑perimental group, while the control group did not include the domain dictionary. Bothgroups underwent word segmentation and stop word removal processes, following thesame procedures outlined in Section 2.1.(2) The dataset was split into a training set (700 entries) and a test set (300 entries) ata ratio of 7:3.(3) The TF‑IDF algorithmwas applied to construct a text feature matrix for the corpus,which was then input into the SVM model for training to generate a text classifier.(4) The test set was used to evaluate the performance of the classifier, with accuracy,recall, and F1‑score serving as the evaluation metrics.The experimental results demonstrate that the performance of the text classifier signif‑icantly improved after loading the domain dictionary. The detailed results are presentedin Table 5.Table 5. Evaluation Results of Text Classifier.Lexicon P/% R/% F1/%Loading 91.0 95.3 93.1Unloading 82.4 85.5 83.9It can be observed that after loading the domain‑specific dictionary, the classifier’saccuracy improved by 8.6%, recall by 9.8%, and F1‑score by 9.2%. This confirms the effec‑tiveness of the domain dictionary in enhancing the accuracy of text classification.Appl. Sci. 2025, 15, 4134 13 of 16To further validate the rationality of the number of seed vocabulary, we adjusted thequantity of seed vocabulary and monitored changes in the F1‑score. The specific experi‑mental design is as follows:We adjusted the high‑frequency terms W(t) ≥ 0.85 × max(W) obtained from the TF‑IDF algorithm results described in Section 2.2. For the control group, we experimentedwith different thresholds: 0.8, 0.825, 0.875, and 0.9. These thresholds respectively resultedin seed vocabularies of sizes 4,567, 3,996, 2,854, and 2,284. At each level of seed term quan‑tity, we repeated the aforementioned text classification experiments, recording Precision(P), Recall (R), and F1‑score. The experimental results are illustrated in Figure 6.Appl. Sci. 2025, 15, x FOR PEER REVIEW 13 of 16 Figure 6. Results of sensitivity analysis. The F1-score peaked at 93.1% when the seed vocabulary’s total was 3425. Increasing the frequency coefficient to reduce the number of seed vocabulary led to a significant de-cline in the F1-score (dropping to a minimum of 91.1%), indicating inadequate vocabulary coverage. Conversely, decreasing the frequency coefficient to increase the seed vocabu-lary’s total resulted in the stabilization of the F1-score. It is evident that the current seed vocabulary total (3425) achieves an optimal balance in the F1-score, ensuring adequate dictionary coverage while avoiding excessive consumption of computational resources. Through text classification experiments and sensitivity analysis, we have validated the effectiveness of the domain-specific dictionary constructed in this study for improving the accuracy of document recognition in the field of power design. Additionally, the sen-sitivity analysis demonstrates that the current seed vocabulary total (3426) achieves an optimal balance in the F1-score, providing strong support for the practical application of the lexicon in engineering contexts. 3.6. Summary of Results Combining both automated evaluation and expert validation, we can conclude that our framework achieves a high level of semantic accuracy suitable for practical engineer-ing applications. Specifically, Automated Evaluation: The average cosine similarity of 0.86 and the high proportion (84.3%) of pairs with similarity scores ≥ 0.75 demonstrate strong semantic consistency among the generated synonyms. Expert Validation: An accuracy rate of 89.3% confirms the reliability of the synonym generation process, validating the effectiveness of the LLM-guided iterative optimization mechanism. Dictionary Effectiveness and Sensitivity Analysis: Loading the domain dictionary significantly improved the classifier’s performance, with accuracy increasing by 8.6%, re-call by 9.8%, and F1-score by 9.2%. The sensitivity analysis further confirmed that the cur-rent seed vocabulary total (3425) achieves an optimal balance in the F1-score. These findings support the robustness and applicability of the constructed lexicon in the field of urban power grid design. The integration of advanced NLP techniques with domain expertise ensures that the final lexicon is both comprehensive and precise, provid-ing a solid foundation for intelligent systems in this specialized domain. Figure 6. Results of sensitivity analysis.The F1‑score peaked at 93.1% when the seed vocabulary’s total was 3425. Increasingthe frequency coefficient to reduce the number of seed vocabulary led to a significant de‑cline in the F1‑score (dropping to a minimum of 91.1%), indicating inadequate vocabularycoverage. Conversely, decreasing the frequency coefficient to increase the seed vocabu‑lary’s total resulted in the stabilization of the F1‑score. It is evident that the current seedvocabulary total (3425) achieves an optimal balance in the F1‑score, ensuring adequate dic‑tionary coverage while avoiding excessive consumption of computational resources.Through text classification experiments and sensitivity analysis, we have validatedthe effectiveness of the domain‑specific dictionary constructed in this study for improv‑ing the accuracy of document recognition in the field of power design. Additionally, thesensitivity analysis demonstrates that the current seed vocabulary total (3426) achieves anoptimal balance in the F1‑score, providing strong support for the practical application ofthe lexicon in engineering contexts.3.6. Summary of ResultsCombining both automated evaluation and expert validation, we can conclude thatour fra ework achieves a high level of semantic accuracy suitable for practical engineeringapplications. Specifically,Automated Evaluation: The average cosine similarity of 0.86 and the high propor‑tion (84.3%) of pairs with similarity scores≥ 0.75 demonstrate strong semantic c nsistencyamong the generated synonyms.Expert Validation: An accuracy rate of 89.3% confirms the reliability of the synonymgeneration process, validating the effectiveness of the LLM‑guided iterative optimizationm chanism.Appl. Sci. 2025, 15, 4134 14 of 16Dictionary Effectiveness and Sensitivity Analysis: Loading the domain dictionary sig‑nificantly improved the classifier’s performance, with accuracy increasing by 8.6%, recallby 9.8%, and F1‑score by 9.2%. The sensitivity analysis further confirmed that the currentseed vocabulary total (3425) achieves an optimal balance in the F1‑score.These findings support the robustness and applicability of the constructed lexicon inthe field of urban power grid design. The integration of advancedNLP techniqueswith do‑main expertise ensures that the final lexicon is both comprehensive and precise, providinga solid foundation for intelligent systems in this specialized domain.4. Conclusions4.1. Research SummaryThis study addresses the challenges of low‑term coverage, semantic ambiguity, andoutdated updates in constructing a lexicon for urban power grid design. We propose amulti‑level terminology extraction and synonym expansion framework based on LargeLanguage Models (LLMs). By building a structured corpus tailored to the power domainand integrating an improved mutual information and adjacency entropy filtering algo‑rithm, we achieve precise extraction of high‑quality seed vocabulary. Innovatively, weleverage the semantic understanding capabilities of LLMs and introduce a self‑correctionmechanism to significantly enhance the accuracy and efficiency of synonym mining. Ex‑perimental results demonstrate that this method effectively constructs a comprehensiveand semantically consistent lexicon for power grid design, providing robust support forthe semantic parsing capabilities of intelligent design systems.The novelty of this study lies in the following:In terms of methodology, this study proposes a three‑level collaborative architectureof “statistical filtering + LLM semantic extension + self‑correction”, which realizes the com‑plementary advantages of statistical methods and LLM in the field of power design.In terms of achievements, this study has constructed the first terminology dictionaryin the field of power design that integrates compound words and named entities, form‑ing the foundation of a smart grid design system, and has been validated in engineeringpracticality.4.2. Main Conclusions1. Enhanced Effectiveness of Domain Lexicon Construction with LLMs:By designing structured prompt templates and a self‑correction mechanism, LLMsexhibit strong contextual reasoning capabilities in synonym mining. The average cosinesimilarity of generated term pairs is 0.86, and the human validation accuracy exceeds 89%,indicating high reliability and precision.2. Improved Engineering Practicality:The constructed lexicon covers 3426 core seed vocabulary and 10,745 synonyms, sub‑stantially addressing the existing gaps in power‑related dictionaries. This enhances prac‑tical applications like building domain knowledge graphs for better information retrieval,thereby improving smart Q&A systems with more accurate query interpretation and en‑abling semantic parsing in automated design systems for precise and efficient design gen‑eration, and it provides a solid foundation for the practical application of intelligent designsystems in engineering scenarios.4.3. Limitations and Future Directions1. Corpus Limitations:The current corpus primarily relies on national and industry standard texts, lackingactual engineering case studies and dynamically updated design documents. Future workAppl. Sci. 2025, 15, 4134 15 of 16should extend the corpus to include multi‑source heterogeneous data to enhance the real‑time relevance and adaptability of the lexicon to various scenarios.2. Model Generalization Challenges:The current approach has a strong dependency on specific LLM architectures, requir‑ing further validation of its transferability across different LLM frameworks. Exploringlightweight model deployment solutions can help improve scalability and efficiency.3. Insufficiencies in Semantic Depth Understanding:Current synonym verification primarily relies on word vector similarity and manualchecks. Future work could integrate knowledge graphs specific to the power domain tobuild amultimodal semantic association network, thereby enhancing the fine‑grainedpars‑ing of term relationships.Author Contributions: Conceptualization, Y.X. and X.C.; methodology, Y.X., X.C., B.Z. and Z.W.;software, T.W. and Y.Y.; validation, Y.X., X.C. and Z.H.; formal analysis, Z.H.; investigation, X.Z andX.C.; resources, Y.X., T.W., Y.Y. andZ.H.; data curation, B.Z.; writing—original draft preparation, Y.X.and X.Z.; writing—review and editing, X.C. and B.Z.; visualization, X.Z.; supervision, B.Z.; projectadministration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the publishedversion of the manuscript.Funding: This research was funded by the science and technology project of State Grid Jiangsu Elec‑tric Power Design Consulting Co., Ltd. (Project No.: JW202307).Institutional Review Board Statement: Not applicable.Informed Consent Statement: Not applicable.Data Availability Statement: This research utilized a proprietary dataset provided by State GridJiangsu Electric Power Design Consulting Co., Ltd. Due to the sensitive commercial informationcontained within, we are unable to make the original dataset publicly available. However, we haveprovided detailed processing procedures and analytical methods to enable other researchers to re‑produce our work on similar datasets.Acknowledgments: The authors thank the State Grid Jiangsu Electric Power Design and ConsultingCo., Ltd., for the funding support provided by the science and technology project.Conflicts of Interest: Authors Yan Xu, Tao Wang, Yang Yuan and Ziyue Huang declare that theywork in State Grid Jiangsu Electric Power Design Consulting Co., Ltd. The funders participatedin the design of the study, as well as in the analyses and interpretation of data, the writing of thisarticle or the decision to submit it for publication. The remaining authors declare that the researchwas conducted in the absence of any commercial or financial relationships that could be construedas a potential conflict of interest.AbbreviationsThe following abbreviations are used in this manuscript:LLM Large Language ModelMI Mutual InformationBERT Bidirectional Encoder Representations from TransformersAPI Application Programming InterfaceReferences1. Fan, H.; Fan, S.; Li, B.; Feng, C.; Luo, H. Research on Construction of Professional Dictionary in Power Dispatching Field. Electr.Power Inf. Commun. Technol. 2021, 19, 57–65.2. Cheng, Y.; Huang, Y. Research and development of domain dictionary construction system. In Proceedings of the internationalconference on web intelligence, Leipzig, Germany, 23 August 2017; pp. 1162–1165.Appl. Sci. 2025, 15, 4134 16 of 163. Yi, R.; Zhang, T.; Xing, X.; Ma, W.; Zhang, K.; Liu, W. Construction of a disaster and accident domain dictionary integratingstandard knowledge. China Stand. 2022, 15, 88–94.4. Qi, W.; Yang, L. Research on the Network Public Opinion Risk Assessment of Social Security Events Incorporating Domain RiskDictionary. Time Phys. Int. Workshop 2024, 47, 175–183.5. Zhang, Z.; Mao, C.; Yu, Z.; Huang, Y.; Liu, Y. Sensitive judicial public opinion information recognition with the domain termi‑nology dictionary. J. Chin. Inf. Process. 2022, 36, 1–18, 27.6. Zhao, X.; Hu, Y.; Qin, T.; Wan, W.; Wang, Y. A Domain‑Specific Lexicon for Improving Emergency Management in Gas PipelineNetworks through Knowledge Fusing. Appl. Sci. 2024, 14, 8094. [CrossRef]7. Wang, Z.; Wang, D.; Li, Q. Keyword extraction from scientific research projects based on SRP‑TF‑IDF. Chin. J. Electron. 2021, 30,652–657.8. Lee, K.H.; Baek, R.J.; Kim, W.S. A Study on Applying Novel Reverse N‑Gram for Construction of Natural Language ProcessingDictionary for Healthcare Big Data Analysis. J. Converg. Cult. Technol. 2024, 10, 391–396.9. Liu, W.; Liu, P.; Liu, W.; Li, N. A newword discovery algorithm based on mutual information and adjacency entropy. Appl. Res.Comput. 2019, 36, 1293–1296.10. Shen, Z.; Chao, Y.; Li, X.; Zhang, W. Research on new word discovery methods for specific domains. Comput. Simul. 2022, 39,269–273.11. Song, X.‑Y.; Zhao, Y.; Jin, L.‑T.; Sun, Y.; Liu, T. Research on the construction of sentiment dictionary based on Word2vec. InProceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China, 21–23December 2018; pp. 1–6.12. Zixiao, Z.; Mao, K. Knowledge‑based BERT word embedding fine‑tuning for emotion recognition. Neurocomputing 2023, 552,126488.13. Ramage, D.; Rafferty, A.N.; Manning, C.D. Random walks for text semantic similarity. In Proceedings of the 2009 workshop ongraph‑based methods for natural language processing (TextGraphs‑4), Stanford, CA, USA, 7 August 2009; pp. 23–31.14. Minkov, E.; Cohen, W. Graph based similarity measures for synonym extraction from parsed text. In Proceedings of theTextGraphs‑7: Graph‑based Methods for Natural Language Processing, Jeju, Republic of Korea, 12 July 2012; pp. 20–24.15. Guo, S.; Wang, Q.; Wang, L.; Wang, B.; Guo, L. Knowledge Graph Embedding with Iterative Guidance from Soft Rules. Proc.AAAI Conf. Artif. Intell. 2018, 32, 11918. [CrossRef]16. Church, K.; Hanks, P. Word association norms, mutual information, and lexicography. Comput. Linguist. 1990, 16, 22–29.17. Hamilton, W.L.; Leskovec, J.; Jurafsky, D. Diachronic word embeddings reveal statistical laws of semantic change. arXiv 2016,arXiv:1605.09096.18. Yu, H.F.; Hsieh, C.J.; Yun, H.; Vishwanathan, S.V.N.; Dhillon, I.S. A scalable asynchronous distributed algorithm for topic mod‑eling. In Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, 18–22 May 2015; pp. 1340–1350.19. GB/T 50934‑2013; Technical Code for Seepage Prevention in Petrochemical Engineering. Available online: https://www.codeofchina.com/standard/GBT50934‑2013.html (accessed on 5 April 2025).20. DL/T 5218‑2012; Technical Code for the Design of 220 kV~750 kV Substation. Available online: https://www.chinesestandard.net/PDF.aspx/DLT5218‑2012 (accessed on 5 April 2025).21. DL/T 1033‑2016; Electricity—Vocabulary—Part 4: Thermal Power Generation. Available online: https://www.transcustoms.cn/GB_standards/GB_standards_cn.asp?code=DL/T%201033.4‑2016 (accessed on 5 April 2025).22. Salton, G.; Buckley, C. Term‑weighting approaches in automatic text retrieval. Inf. Process. Manag. 1988, 24, 513–523. [CrossRef]23. Cover, T.M.; Thomas, J.A. Elements of Information Theory; Wiley: New Tork, NY, USA, 1991.24. Huang, J.H.; Powers, D. Chinese word segmentation based on contextual entropy. In Proceedings of the 17th Asian PacificConference on Language, Information and Computation, Singapore, 1–3 October 2003; pp. 152–158.25. Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al.Language models are few‑shot learners. Adv. Neural Inf. Process. Syst. 2020, 33, 1877–1901.Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual au‑thor(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury topeople or property resulting from any ideas, methods, instructions or products referred to in the content.
Article
eng
Reviewed
Vancouver : University of British Columbia Library
Multidisciplinary Digital Publishing Institute
CC BY 4.0
Faculty
Researcher
electric power grid design
domain-specific lexicon
synonym mining
large language models
natural language processing
LLM-Enhanced Framework for Building Domain-Specific Lexicon for Urban Power Grid Design
Text