An entropy-based query expansion approach for learning researchers’ dynamic information needs

作者：

Highlights：

• Proposing an entropy-based query expansion with a reweighting (E_QE) approach.

• The E_QE used to learn the researchers’ evolving information needs at different levels of topic change.

• Adopting a simulation pseudo-relevance feedback process to evaluate the proposed approach.

• The results show that the proposed E_QE approach can achieve better search results than the TFIDF.

摘要

•Proposing an entropy-based query expansion with a reweighting (E_QE) approach.•The E_QE used to learn the researchers’ evolving information needs at different levels of topic change.•Adopting a simulation pseudo-relevance feedback process to evaluate the proposed approach.•The results show that the proposed E_QE approach can achieve better search results than the TFIDF.

论文关键词：Query expansion,Pseudo-relevance feedback,Term weighting,Topic change,Entropy

论文评审过程：Received 12 January 2013, Revised 17 July 2013, Accepted 23 July 2013, Available online 1 August 2013.

论文官网地址：https://doi.org/10.1016/j.knosys.2013.07.017