Article(id=1281685541645169484, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.06.1237, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739548800000, receivedDateStr=2025-02-15, revisedDate=1744992000000, revisedDateStr=2025-04-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1783507245694, onlineDateStr=2026-07-08, pubDate=1751040000000, pubDateStr=2025-06-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783507245694, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783507245694, creator=13701087609, updateTime=1783507245694, updator=13701087609, issue=Issue{id=1281685530169545672, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='6', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1751040000000', pubDateStr='2025-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783507242958, creator='13701087609', updateTime=1783589309504, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282029742274622413, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282029742274622414, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281685530169545672, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=51, endPage=59, ext={EN=ArticleExt(id=1281685541842301773, articleId=1281685541645169484, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Extracting construction safety requirement information using natural language processing, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

To solve the problem that construction safety requirement information hidden in project documents is hard to be discovered without relevance and semantic ambiguity, a two-stage integration framework combining NLP techniques was developed for project document analysis and classification and extraction of requirement information. First, the safety targets of the project to be evaluated were obtained by combining the multivariate techniques of NLP, and an association model was established based on the topic model to recommend the appropriate requirement types. Then, the semantic features of the three types of elements were considered, and keyword analysis, sentiment analysis, and dependency analysis were adopted to extract the three types of elements, respectively. Finally, two types of construction projects (civil and industrial) were used as case to test the type recommendation and extraction of construction safety requirements. The results show that the two-stage integration framework recommends four appropriate requirement types for civil and industrial buildings respectively, and the combination of lexical properties and lexical sentiment can effectively extract the requirement keywords and behavior opinion words, and the extraction accuracy of the main elements can reach 88.6% after supplementing the description of building types. The test results confirm that responding to safety target features can recommend suitable types from the complicated requirement information, and the classification and extraction of requirement information combined with NLP avoids subjective preferences and improves the accuracy of information extraction.

, authors=Zhijiang WU¹, Mengyao LIU¹, Guofeng MA², authorsList=Zhijiang WU, Mengyao LIU, Guofeng MA, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281685542832157520, articleId=1281685541645169484, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于自然语言处理的施工安全需求信息提取, columnId=1149733271296193071, journalTitle=中国安全科学学报, columnName=安全社会科学与安全管理, runingTitle=null, highlight=null, articleAbstract=

为解决潜藏在项目文档中的施工安全需求信息因欠缺关联性、语义模糊而难以被发掘的问题，开发一种结合自然语言处理（NLP）技术的2阶段集成框架，用于项目文档分析并进行需求信息的分类提取。首先，结合NLP的多元技术获取待评估工程的安全目标，并基于主题模型建立关联模型以推荐合适的施工安全需求类型；然后，考虑3类要素的语义特征，分别采用关键词分析、情感分析以及依存关系分析对3类要素进行提取；最后，以2类建筑工程（民用和工业建筑）为例，开展施工安全需求的分类提取测试。结果表明：2阶段集成框架分别为民用建筑和工业建筑推荐到4种合适的需求类型，结合词性和词汇情感色彩能够有效提取到需求关键词和行为观点词，并且主体要素在补充建筑类型说明后的提取精度可达到88.6%；测试结果证实响应安全目标特征能够从繁杂的施工安全需求信息中推荐出适宜类型，而结合自然语言处理的需求信息分类提取可以避免主观偏好以提高信息提取精度。

, authors=吴志江讲师¹, 刘梦瑶¹, 马国丰教授², authorsList=吴志江讲师, 刘梦瑶, 马国丰教授, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=sE8lOTFijX0/REX/XWnJGA==, magXml=Vm19xHhg31MhSmTpQwFtww==, pdfUrl=null, pdf=R9N+RLksi17/BmsISswXzw==, pdfFileSize=5763823, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=azgPZmxZwQMQs8Zwb1g5lg==, mapNumber=null, fund=null)}, authors=[Author(id=1281685543213839192, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281685541645169484, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zjwu@yzu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281685543306113882, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281685541645169484, authorId=1281685543213839192, language=EN, stringName=Zhijiang WU, firstName=Zhijiang, middleName=null, lastName=WU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹College of Architectural Science and Engineering, Yangzhou University, Yangzhou Jiangsu 225009, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281685543369028443, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281685541645169484, authorId=1281685543213839192, language=CN, stringName=吴志江讲师, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹扬州大学 建筑科学与工程学院，江苏 扬州 225009, bio={"content":"

吴志江（1992—），男，江西鄱阳人，博士，讲师，硕士生导师，主要从事人机交互安全与协同方面的研究。E-mail: zjwu@yzu.edu.cn。

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吴志江（1992—），男，江西鄱阳人，博士，讲师，硕士生导师，主要从事人机交互安全与协同方面的研究。E-mail: zjwu@yzu.edu.cn。

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