论文类型：会议论文
收录刊物：CPCI-S、EI
卷号：9994
页面范围：125-137
关键字：Information Retrieval; Query expansion; Learning to rank; Patent retrieval
摘要：Query expansion methods have been proven to be effective to improve the average performance of patent retrieval, and most of query expansion methods use single source of information for query expansion term selection. In this paper, we propose a method which exploits external resources for improving patent retrieval. Google search engine and Derwent World Patents Index were used as external resources to enhance the performance of query expansion methods. LambdaRank was employed to improve patent retrieval performance by combining different query expansion methods with different text fields weighting strategies of different resources. Experiments on TREC data sets showed that our combination of multiple information sources for query formulation was more effective than using any single source to improve patent retrieval performance.