Abstract
Question answering systems rely on retrieval components to identify documents that contain an answer to a user’s question. The formulation of queries that are used for retrieving those documents has a strong impact on the effectiveness of the retrieval component. Here, we focus on predicting the importance of terms from the original question. We use model tree machine learning techniques in order to assign weights to query terms according to their usefulness for identifying documents that contain an answer. Incorporating the learned weights into a state-of-the-art retrieval system results in statistically significant improvements.
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Monz, C. (2007). Model Tree Learning for Query Term Weighting in Question Answering. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_55
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DOI: https://doi.org/10.1007/978-3-540-71496-5_55
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