Improved answer ranking in social question-answering portals

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Improved answer ranking in social question-answering portals
Community QA portals provide an important resource for non-factoid question-answering. The inherent noisiness of user-generated data makes the identification of high-quality content challenging but all the more important. We present an approach to answer ranking and show the usefulness of features that explicitly model answer quality. Furthermore, we introduce the idea of leveraging snippets of web search results for query expansion in answer ranking. We present an evaluation setup that avoids spurious results reported in earlier work. Our results show the usefulness of our features and query expansion techniques, and point to the importance of regularization when learning from noisy data. Categories and Subject Descriptors H.3.4 [Information Storage and Retrieval]: Systems and Software—Question-answering (fact retrieval) systems General Terms Experimentation, Performance Keywords Social search and ranking algorithms; Community question answering; Query expansion
Felix Hieber, Stefan Riezler
Added 13 Dec 2011
Updated 13 Dec 2011
Type Journal
Year 2011
Where CIKM
Authors Felix Hieber, Stefan Riezler
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