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基于多层Markov网络的信息检索模型 被引量:7

An Information Retrieval Model Based on Multilayer Markov Network
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摘要 随着信息检索技术的不断发展,挖掘更加有效的信息来提高检索精度成为研究热点,已有的研究表明在检索过程中有效地融合各种信息将得到更好的检索效果。对一个具体查询而言,可以充分利用与已有查询的相关性、词语相关性和文档相关性等信息进行查询扩展和重构。基于这种思路,该文分别构造查询网络、词网络和文档网络,提出了多层Markov网络的信息检索模型,模型可以融合词间关系、文档间关系和查询间关系,为了有效降低计算量,给出了基于团计算模型。在标准数据集上的实验表明该文的模型能够有效融合三类信息,并较大幅度地提高检索效果。 The information retrieval usually can be improved by combining more information mined from the retrieval process.To fully take advantage of the existing queries correlation information,terms and documents for query expansion and reconstruction,we propose an information retrieval model based on multilayer Markov network.The Markov network is constructed by the correlation of query network,term network and document network.A clique model is further designed to speed up the computation.The experiments on the standard data sets have indicated that our model can integrate information of three aspects effectively for an improved effect of retrieval.
出处 《中文信息学报》 CSCD 北大核心 2016年第1期56-62,共7页 Journal of Chinese Information Processing
基金 国家自然科学基金(61272212 61462043 61462045) 江西省自然科学基金(20122BAB211032 20151BAB217014) 江西省高校人文社会科学青年基金(JC1312)
关键词 信息检索 多层Markov网络 查询扩展 information retrieval multilayer Markov network query expansion clique
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