关键词: 信息检索, 查询扩展, 排序学习, 专利检索

Abstract:  Query expansion is wildly used in information retrieval systems. In order to improve patent retrieval results, this paper applies query expansion methods for optimization. After training the word embedding models using relevant documents, words for query expansion are selected based on the similarities with original query. This paper proposes four methods to select query expansion terms by applying word embedding, and proposes two methods to rank the terms by relevance to the query. These methods are used to improve the existing query expansion methods. The expe-riments conducted on TREC dataset indicate that combining traditional TF-IDF expansion method with the proposed    approach can improve the performance of query expansion models, leading to a better understanding of query intent. 

Key words: information retrieval, query expansion, learning to rank, patent retrieval