基于统计语言模型的个性化语义检索系统

Xianghao Meng, Dongmei Li, Qichen Han
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引用次数: 0

摘要

将同义词库中的语义关系引入到现有的网络信息检索工具中,实现语义检索。使用统计语言模型来表达查询语句,并以概率分布的形式返回结果,可以更有效地完成用户模型的构建,实现个性化检索。首先,本文提出了一种基于词库中词间关系的相似度计算方法。在此基础上,结合查询扩展和加权排序的思想,提出了一种基于词库的林业信息语义检索方法。其次,利用统计语言模型提出了基于主题模型、历史模型和混合模型三种不同用户模型的个性化检索方法。最后,利用语义检索和个性化检索方法,实现了林业信息个性化语义检索系统。实验结果表明,提出的个性化语义检索方法能有效提高检索性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized Semantic Retrieval System based on Statistical Language Model
The semantic relationship in thesaurus is introduced into the current network information retrieval tool, which can realize semantic retrieval. Using a statistical language model to express query statements and return results in the form of probability distribution can more effectively complete the construction of user model and realize personalized retrieval. Firstly, this paper proposes a similarity calculation method based on the relationship between words in the thesaurus. On the basis of this method, combined with the idea of query expansion and weighted sorting, this paper proposes a semantic retrieval method of forestry information based on the thesaurus. Secondly, this paper uses a statistical language model to propose personalized retrieval methods based on three different user models: topic model, historical model and mixed model. Finally, a forestry information personalized semantic retrieval system is realized by using semantic retrieval and personalized retrieval method. Experimental results indicate that the proposed personalized semantic retrieval method can effectively improve the retrieval performance.
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