Abstract
We introduce the topical relevance model (TRLM) as a generalization of the standard relevance model (RLM). The TRLM alleviates the limitations of the RLM by exploiting the multi-topical structure of pseudo-relevant documents. In TRLM, intra-topical document and query term co-occurrences are favoured, whereas the inter-topical ones are down-weighted. The multi-topical nature of pseudo-relevant documents results from the multi-faceted nature of the information need typically expressed in a query. The TRLM provides a framework to estimate a set of underlying hypothetical relevance models for each such aspect of the information need. Experimental results show that the TRLM significantly outperforms the RLM for ad-hoc and patent prior art search.
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Ganguly, D., Leveling, J., Jones, G.J.F. (2012). Topical Relevance Model. In: Hou, Y., Nie, JY., Sun, L., Wang, B., Zhang, P. (eds) Information Retrieval Technology. AIRS 2012. Lecture Notes in Computer Science, vol 7675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35341-3_28
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DOI: https://doi.org/10.1007/978-3-642-35341-3_28
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