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Argumentative Feedback: A Linguistically-motivated Term Expansion for Information Retrieval
We report on the development of a new automatic feedback model to improve information retrieval in digital libraries. Our hypothesis is that some particular sentences, selected based on argumentative criteria, can be more useful than others to perform well-known feedback information retrieval tasks. The argumentative model we explore is based on four disjunct classes, which has been very regularly observed in scientific reports: PURPOSE, METHODS, RESULTS, CONCLUSION. To test this hypothesis, we use the Rocchio algorithm as baseline. While Rocchio selects the features to be added to the original query based on statistical evidence, we propose to base our feature selection also on argumentative criteria. Thus, we restrict the expansion on features appearing only in sentences classified into one of our argumentative categories. Our results, obtained on the OHSUMED collection, show a significant improvement when expansion is based on PURPOSE (mean average precision = +23%) and CONCLUSION (mean average precision = +41%) contents rather than on other argumentative contents. These results suggest that argumentation is an important linguistic dimension that could benefit information retrieval.