论文名称：A PATENT RETRIEVAL QUERY EXPANSION METHOD BASED ON SEMANTIC DICTIONARY
论文类型：期刊论文
发表刊物：JOURNAL OF NONLINEAR AND CONVEX ANALYSIS
收录刊物：SCIE
卷号：20
期号：6,SI
页面范围：1233-1240
ISSN号：1345-4773
关键字：Learning to rank; query expansion; patent retrieval; LambdaMART; semantic dictionary
摘要：The query expansion can select expanded words to add to the original query based on semantic relationships and terms co-occurrence relationships to better understand the user's query intent. This paper proposes a method of using semantic dictionary WordNet as an external resource for query expansion to improve patent retrieval. LambdaMART method was used to be combined with different query expansion to improve the performance of patent retrieval. Experiments on TREC datasets showed that the learning to rank model, which used WordNet to modify expanded term weights, has better performance and improves the performance of patent retrieval.
发表时间：2019-01-01