Dietz, LauraDalton, Jeff2021-05-042021-05-0420202020http://dx.doi.org/10.1007/s13222-020-00334-yhttps://dl.gi.de/handle/20.500.12116/36388Manually creating test collections is a time-, effort-, and cost-intensive process. This paper describes a fully automatic alternative for deriving large-scale test collections, where no human assessments are needed. The empirical experiments confirm that automatic test collection and manual assessments agree on the best performing systems. The collection includes relevance judgments for both text passages and knowledge base entities. Since test collections with relevance data for both entity and text passages are rare, this approach provides a cost-efficient way for training and evaluating ad hoc passage retrieval, entity retrieval, and entity-aware text retrieval methods.Automatic EvaluationComplex Answer RetrievalEntity and Passage RetrievalText/Journal Article10.1007/s13222-020-00334-y1610-1995