Investigating Retrieval Method Selection with Axiomatic Features
This work addresses the challenge of selecting and combining retrieval methods for better search results, but it is incremental as it builds on existing axiomatic approaches and meta-learning techniques.
The paper tackled the problem of algorithm selection in ad-hoc information retrieval by proposing a meta-learner that predicts how to combine relevance scores from different retrieval methods based on axiomatic features, and found that it often significantly improves over individual methods on TREC Web Track data.
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.