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论文类型： 期刊论文

发表时间： 2013-07-01

发表刊物： Journal of Computational Information Systems

收录刊物： EI、Scopus

卷号： 9

期号： 13

页面范围： 5387-5394

ISSN号： 15539105

摘要： Query expansion methods has been proven to be effective to improve the average performance of patent retrieval. However, many studies have shown that, although query expansion helps many queries, it also hurts many other queries, which limits its usefulness in patent retrieval. Therefore, an important, and yet difficult challenge is to improve the overall effectiveness of query expansion without sacrificing the performance of individual queries too much. This paper proposes a learning to rank based approach to improve the performance of query expansion on patent retrieval by optimizing the combination of a set of query expansion algorithms. Learning to rank approach can accommodate many basic query expansion methods as features. We explore learning to rank approaches to improve query expansion by combining different methods with different text fields weighting strategies. Experimental results on TREC test collection show that the patent retrieval performance can be improved when learning to rank approach is used for query expansion. ? 2013 by Binary Information Press.