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A phased ranking model for question answering

We describe a general result ranking approach for multi-phase, multi strategy information systems, which has been applied to the task of question answering (QA). Many information systems incorporate multiple steps and each step or phase may incorporate multiple component algorithms to achieve acceptable robustness and overall performance. Such systems may produce and rank a large number of candidate results. Prior work includes many models that rank a particular type of information object (e.g. a retrieved document, a factoid answer) using features specific to that information type, without attempting to make use of other non-local features (e.g. features of the upstream information source). We propose an approach that allows each phase in a system to leverage information propagated from preceding phases to inform the ranking decision. This is accomplished by a system object graph which represents all of the objects created during system execution, object dependencies (e.g. provenance), and ranking feature values extracted for a specific object. We evaluate the effectiveness of the proposed ranking approach in a multi-phase question answering system built by recombining pre-existing software modules. Experimental results show that our proposed approach significantly outperforms comparable answer ranking models.