Abstract
Traditional relevance feedback methods, which usually use the most frequent terms in the relevant documents as expansion terms to enrich the user’s initial query, could help improve retrieval performance. However, in reality, many expansion terms identified in traditional approaches are indeed unrelated to the query and even harmful to the retrieval. This paper introduces a new method based on the relative word-frequency to select good expansion terms. The relative word-frequency defined in this paper is a new feature and can help discriminate relevant documents from irrelevant ones. The new approach selects good expansion terms according to the relative word-frequency and uses them to reformulate the initial query. We compare a set of existing relevance feedback methods with our proposed approach, including the representative vector space models and language models. Our experiments on several TREC collections demonstrate that retrieval effectiveness can be much improved when the proposed approach is used. Experimental results show that the improvement of our proposed approach is more than 30% over traditional relevance feedback techniques.
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Chen, Z., Lu, Y. (2010). A Relative Word-Frequency Based Method for Relevance Feedback. In: Dicheva, D., Dochev, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2010. Lecture Notes in Computer Science(), vol 6304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15431-7_18
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DOI: https://doi.org/10.1007/978-3-642-15431-7_18
Publisher Name: Springer, Berlin, Heidelberg
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