Discriminative probabilistic models for passage based retrieval

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Discriminative probabilistic models for passage based retrieval
The approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with different characteristics. One main reason of the inconsistent performance is that there exists no unified framework to model the evidence of individual passages within a document. This paper proposes two probabilistic models to formally model the evidence of a set of top ranked passages in a document. The first probabilistic model follows the retrieval criterion that a document is relevant if any passage in the document is relevant, and models each passage independently. The second probabilistic model goes a step further and incorporates the similarity correlations among the passages. Both models are trained in a discriminative manner. Furthermore, we present a combination approach to combine the ranked lists of document retrieval and passage-based retrieval. An extensive set of experiments have been conducted on four different TREC test beds t...
Mengqiu Wang, Luo Si
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Authors Mengqiu Wang, Luo Si
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