Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Relevance and contributing information types of searched documents in task performance
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Automatic query expansion via lexical-semantic relationships
Journal of the American Society for Information Science and Technology
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Information Processing and Management: an International Journal
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Literature survey is one of the most important steps in the process of academic research, allowing researchers to explore and understand topics. However, researchers without sufficient prior knowledge lack the skills to determine proper and accurate keywords for investigating the topics at hand. To tackle this problem, we proposed an entropy-based query expansion with a reweigh ting (E_QE) approach to revise queries during the iterative retrieval process. We designed a series of experiments that consider the researcher's changing information needs during task execution. Two topic change situations are considered in this work隆Xboth minor, and dramatic topic changes. The simulation-based pseudo-relevance feedback technique is applied during the search process to evaluate the effectiveness of the proposed approach without the intervention of human efforts. We measured the effectiveness of the TFIDF and E_QE approaches for different types of topic change situations. The preliminary results show that the proposed query expansion approach can achieve better results, helping researchers to revise queries.