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Indexed by:Journal Papers

Date of Publication:2019-01-01

Journal:JOURNAL OF NONLINEAR AND CONVEX ANALYSIS

Included Journals:SCIE

Volume:20

Issue:6,SI

Page Number:1233-1240

ISSN No.:1345-4773

Key Words:Learning to rank; query expansion; patent retrieval; LambdaMART; semantic dictionary

Abstract: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.