  We consider algorithm selection in the context of ad-hoc information
retrieval. Given a query and a pair of retrieval methods, we propose a
meta-learner that predicts how to combine the methods' relevance scores into an
overall relevance score. Inspired by neural models' different properties with
regard to IR axioms, these predictions are based on features that quantify
axiom-related properties of the query and its top ranked documents. We conduct
an evaluation on TREC Web Track data and find that the meta-learner often
significantly improves over the individual methods. Finally, we conduct feature
and query weight analyses to investigate the meta-learner's behavior.
