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- W14861301 abstract "This thesis presents a novel approach to answering natural language list questions, or questions that have more than a single correct answer. It is based on the hypothesis that the set of answers to a list question will often occur in a similar context. By analyzing candidate answers produced by an existing Question Answering system, it is possible to identify the common context shared by two or more candidate answers. Once the common context is identified, it is then possible to extrapolate from this common context to identify more answer candidates previously not found by the original Question Answering system." @default.
- W14861301 created "2016-06-24" @default.
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- W14861301 date "2005-01-01" @default.
- W14861301 modified "2023-09-27" @default.
- W14861301 title "Improving Answer Precision and Recall of List Questions" @default.
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