基于篇章的名词省略恢复研究及其在机械产品设计中的应用
发布时间：2018-12-15 06:26
【摘要】：机械产品设计未来的发展趋势之一是智能化,这就需要具有知识处理能力的专家系统通过人工智能技术快速有效地收集并分析用户的需求信息,将用户需求转化为产品概念设计要求,从而设计出满足用户需求的产品。 本文将人工智能语言信息处理领域的核心课题与技术——自然语言理解技术应用于机械产品设计中,讨论并研究了基于篇章的名词省略现象及其恢复策略。针对在省略中占82%的名词主语省略、宾语省略两种类型,笔者分别从自然语言理解的句法分析、语义理解和篇章分析三个层面进行研究,并结合对语料库实证性研究的统计数据,总结段落范围内主语省略和宾语省略的一般类型的恢复框架和六种特殊类型的恢复策略,利用篇章理解的主题信息对恢复的正确性进行验证,从而设计出名词省略恢复的整体模型。 最后,把名词省略恢复模块整合到专家系统中,应用于现代机械产品设计中,并对用户需求取得较好的理解结果,为后续产品设计的标准化和智能化提供支持。
[Abstract]:One of the developing trends of mechanical product design in the future is intellectualization, which requires expert system with knowledge processing ability to collect and analyze user demand information quickly and effectively through artificial intelligence technology. The user requirement is transformed into the product conceptual design requirement, and the product that meets the user's demand is designed. In this paper, the core subject and technology of artificial intelligence language information processing, natural language understanding technology, is applied to mechanical product design, and the phenomenon of noun ellipsis based on text and its recovery strategy are discussed and studied. In view of the two types of noun subject ellipsis and object ellipsis which account for 82% of the ellipsis, the author studies them from three aspects: syntactic analysis, semantic understanding and text analysis. Combined with the statistical data of corpus empirical study, the paper summarizes the general types of restoration framework of subject ellipsis and object ellipsis in paragraph scope and six special types of recovery strategies. The correctness of restoration is verified by the topic information of text comprehension, and a global model of noun ellipsis restoration is designed. Finally, the noun ellipsis recovery module is integrated into the expert system and applied to the modern mechanical product design, and the result of understanding the user's requirements is obtained, which provides the support for the standardization and intelligence of the subsequent product design.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TP391.1;TH122
本文编号：2380136
[Abstract]:One of the developing trends of mechanical product design in the future is intellectualization, which requires expert system with knowledge processing ability to collect and analyze user demand information quickly and effectively through artificial intelligence technology. The user requirement is transformed into the product conceptual design requirement, and the product that meets the user's demand is designed. In this paper, the core subject and technology of artificial intelligence language information processing, natural language understanding technology, is applied to mechanical product design, and the phenomenon of noun ellipsis based on text and its recovery strategy are discussed and studied. In view of the two types of noun subject ellipsis and object ellipsis which account for 82% of the ellipsis, the author studies them from three aspects: syntactic analysis, semantic understanding and text analysis. Combined with the statistical data of corpus empirical study, the paper summarizes the general types of restoration framework of subject ellipsis and object ellipsis in paragraph scope and six special types of recovery strategies. The correctness of restoration is verified by the topic information of text comprehension, and a global model of noun ellipsis restoration is designed. Finally, the noun ellipsis recovery module is integrated into the expert system and applied to the modern mechanical product design, and the result of understanding the user's requirements is obtained, which provides the support for the standardization and intelligence of the subsequent product design.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TP391.1;TH122
【引证文献】
中国硕士学位论文全文数据库 前1条
1 严羽;自然语言理解中并列名词歧义消解及其在智能仪器设计领域的应用[D];西安电子科技大学;2011年
，本文编号：2380136
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