Applying Maximum Entropy to Known-Item Email Retrieval

13 years 7 months ago
Applying Maximum Entropy to Known-Item Email Retrieval
It is becoming increasingly common in information retrieval to combine evidence from multiple resources to compute the retrieval status value of documents. Although this has led to considerable improvements in several retrieval tasks, one of the outstanding issues is estimation of the respective weights that should be associated with the different sources of evidence. In this paper we propose to use maximum entropy in combination with the limited memory LBFG algorithm to estimate feature weights. Examining the effectiveness of our approach with respect to the known-item finding task of enterprise track of TREC shows that it significantly outperforms a standard retrieval baseline and leads to competitive performance.
Sirvan Yahyaei, Christof Monz
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ECIR
Authors Sirvan Yahyaei, Christof Monz
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