Document (#39930)

Author

Xu, B.
Lin, H.
Lin, Y.

Title

Assessment of learning to rank methods for query expansion

Source

Journal of the Association for Information Science and Technology. 67(2016) no.6, S.1345-1357

Year

2016

Abstract

Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state-of-the-art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.

Content

Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23476/abstract.

Theme

Semantisches Umfeld in Indexierung u. Retrieval
Retrievalalgorithmen

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4. Karisani, P.; Rahgozar, M.; Oroumchian, F.: Transforming LSA space dimensions into a rubric for an automatic assessment and feedback system (2016) 0.47

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5. Hancock-Beaulieu, M.; Fieldhouse, M.; Do, T.: ¬An evaluation of interactive query expansion in an online library catalogue with a graphical user interface (1995) 0.44

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