基于稳定性语义聚类的相关模型估计

针对由不稳定聚类估计的相关模型影响检索性能的问题,提出了基于稳定性语义聚类的相关模型(SSRM)。首先利用初始查询前N个结果文档构成反馈数据集;然后探测数据集中稳定的语义类别数量;接着从稳定性语义聚类中选择与用户查询最相似的语义类别估计SSRM;最后通过实验对模型的检索性能进行了验证。对TREC数据集5个子集的实验结果显示,SSRM相比相关模型(RM)、语义相关模型(SRM),平均准确率(MAP)性能最少提高了32.11%和0.41%;相比基于聚类的文档模型(CBDM)、基于LDA的文档模型(LBDM)和Resampling等基于聚类的检索方法,MAP性能最少提高了23.64%,19.59%和8.03%。实验结果表明,SSRM有利于改善检索性能。

Relevance model estimation based on stable semantic clustering

To solve the problem of relevance model based on unstable clustering estination and its effect on retrieval performance, a new Stable Semantic Relevance Model (SSRM) was proposed. The feedback data set was first formed by using the top N documents from user initial query, after the stable number of semantic clusters had been detected, SSRM was estimated by those stable semantic clusters selected according to higher user-query similarity. Finally, the SSRM retrieval performance was verified by experiments. Compared with Relevance Model (RM), Semantic Relevance Model (SRM) and the clustering-based retrieval methods including Cluster-Based Document Model (CBDM), LDA-Based Document Model (LBDM) and Resampling, SSRM has improvement of MAP by at least 32.11%, 0.41%, 23.64%,19.59%, 8.03% respectively. The experimental results show that retrieval performance can benefit from SSRM.