Abstract
The task of finding novel information in information retrieval (IR) has been proposed recently and paid more attention to. Compared with techniques in traditional document-level retrieval, query expansion (QE) is dominant in the new task. This paper gives an empirical study on the effectiveness of different QE techniques on finding novel information. The conclusion is drawn according to experiments on two standard test collections of TREC2002 and TREC2003 novelty tracks. Local co-occurrence-based QE approach performs best and makes more than 15% consistent improvement, which enhances both precision and recall in some cases. Proximity-based and dependency-based QE are also effective that both make about 10% progress. Pseudo relevance feedback works better than semantics-based QE and the latter one is not helpful on finding novel information.
Supported by the Chinese Natural Science Foundation (NO. 60223004, 60321002, 60303005), and partially sponsored by the joint project with IBM China research.
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Zhang, M., Lin, C., Ma, S. (2005). How Effective Is Query Expansion for Finding Novel Information? . In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_16
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DOI: https://doi.org/10.1007/978-3-540-30211-7_16
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