The Study of Methods for Language Model Based Positive and Negative Relevance Feedback in Information Retrieval

摘要：

Relevance feedback techniques are important to information retrieval (IR), which can effectively improve the performance of IR. They have been proved by many existing work. The feedback includes positive and negative relevance one. The most of the previous work using feedback have focused on positive relevance feedback and pseudo relevance feedback in IR. In recent years, some work has been done and investigated the negative relevance feedback in IR. However, this paper highlights the incorporation or integration between the language models based positive and negative relevance feedback in IR, where both types of feedback are used to modify and expand the users query model. Our experimental results of using several TREC collections show that this method is significantly outperform the relevance feedback and pseudo relevance feedback in terms of the retrieval accuracy.

关键词： information retrieval language model relevance feedback negative relevance feedback

作者: Jun-yi Wang Xin-ming Ye

作者单位: College of Computer Science of Inner Mongolia University Huhhol,China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 870-873

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）