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ECIR
2007
Springer

Model Tree Learning for Query Term Weighting in Question Answering

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Model Tree Learning for Query Term Weighting in Question Answering
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.
Christof Monz
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ECIR
Authors Christof Monz
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