Boosting Weak Ranking Functions to Enhance Passage Retrieval for Question Answering
Nicolas Usunier, Massih Amini and Gallinari Patrick
In: SIGIR 2004, 25-29 July 2004, Sheffield, England.
We investigate the problem of passage retrieval for 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 using questions from the TREC-11 question/answering track and the Aquaint collection that the proposed algorithm significantly increases both coverage and precision with respect to a conventional IR system.