Boosting Weak Ranking Functions to Enhance Passage Retrieval for Question Answering

Abstract : We investigate the problem of passage retrieval for Question Answering (QA) systems. We adopt a machine learning approach and apply to QA a boosting algorithm initially proposed for ranking a set of objects by combining baseline ranking functions. The system operates in two steps. For a given question, it first retrieves passages using a conventional search engine and assigns each passage a series of scores. It then ranks the returned passages using a weighted feature combination. Weights express the feature importance for ranking and are learned to maximize the number of top ranked relevant passages over a training set. We empirically show that using questions from the TREC-11 question/answering track and the Aquaint collection, the proposed algorithm significantly increases both coverage and precision with respect to a conventional IR system.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01498004
Contributor : Lip6 Publications <>
Submitted on : Wednesday, March 29, 2017 - 4:03:41 PM
Last modification on : Thursday, March 21, 2019 - 1:13:05 PM

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  • HAL Id : hal-01498004, version 1

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Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari. Boosting Weak Ranking Functions to Enhance Passage Retrieval for Question Answering. SIGIR 2004 workshop on Information Retrieval for Question Answering, Jul 2004, Shefield, United Kingdom. ⟨hal-01498004⟩

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