Merging term similarity and term correlativity in retrieval models

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Merging term similarity and term correlativity in retrieval models

Received:2007-06-20 Revised:1900-01-01 Online:2007-12-01 Published:2007-12-01
Contact: Song ZHU

术语相似度和术语相关度在检索模型中的融合研究

徐建民朱松陈富节

河北大学数学与计算机学院河北大学数学与计算机学院河北大学数学与计算机学院

通讯作者: 朱松

Abstract

Abstract: How to improve the retrieval efficiency by means of term relationships was discussed. In order to integrate the specialties of term similarity and term correlativity, a new method to mine the term relationships was proposed. In this method, a correction factor from term correlativity to term similarity was used to adjust the level of relations with the term, which is a more accurate solution to the concept of the terms of semantic matching problem. Applying the new method to two retrieval models, experimental results show that the retrieval efficiency can be improved more obviously.

Key words: Belief network, Bayesian network, term similarity, term correlativity

摘要： 对如何利用术语间的关系提高信息检索系统的性能进行了探讨，分析了术语相似度和术语相关度融合的可行性，设计了一种挖掘术语间关系的新方法，提出了术语相关度对相似度的修正因子，用来调整对术语关系的影响程度，更准确地解决了术语间语义概念的匹配问题。将该方法应用于两种检索模型的实验结果表明，所提方法比单一使用术语相似度或术语相关度的方法具有更好的检索效果。

关键词: 信念网络, 贝叶斯网络, 术语相似度, 术语相关度

CLC Number:

TP311.13

Jian－min XU Song ZHU Fu-jie CHEN. Merging term similarity and term correlativity in retrieval models[J]. Journal of Computer Applications.

徐建民朱松陈富节. 术语相似度和术语相关度在检索模型中的融合研究[J]. 计算机应用.

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