基于混合相关的Markov网络信息检索扩展模型

Mixed Correlation Based Markov Network for Query Expansion in Information Retrieval

查询扩展是提高检索性能的有效方法。为了弥补在数据集中由于词对没有直接出现而导致无法统计出词间关系进行查询扩展的缺陷,该文通过提取Markov网络中的词团信息来量化词间的混合相关性,将强化后的词间混合相关性应用于信息检索扩展模型中。实验表明:基于混合相关的Markov网络信息检索扩展模型的检索效果优于基于直接相关的查询扩展模型;此外,该文提出的模型在总体检索性能上略优于基于团的Markov网络信息检索模型,但在词团提取上大大减少了计算开销。

Query expansion is effective to improve retrieval efficiency. In this paper, the mixed correlation between terms is quantized by term cliques which are obtained from Markov network, so as to solve the computation of the term relationship lack of cooccurence in corpus. The enhanced mixed correlation is then applied to query expansion. The experimental results show that the proposed method outperforms that based on direct correlation. In addition, the method is slightly better than a Markov network model based on cliques significantly reduces the computational overhead of term cliques.