Information retrieval test collections traditionally use a combination of automatic and manual runs to create a pool of documents to be judged. The quality of the final judgments produced for a collection is a product of the variety across each of the runs submitted and the pool depth. In this work, we explore fully automated approaches to generating a pool. By combining a simple voting approach with machine learning from documents retrieved by automatic runs, we are able to identify a large portion of relevant documents that would normally only be found through manual runs. Our initial results are promising and can be extended in future studies to help test collection curators ensure proper judgment coverage is maintained across complete document collections.

The use of sampling, randomized algorithms, or training based on the unpredictable inputs of users in Information Retrieval often leads to non-deterministic outputs. Evaluating the effectiveness of systems incorporating these methods can be challenging since each run may produce different effectiveness scores. Current IR evaluation techniques do not address this problem. Using the context of distributed information retrieval as a case study for our investigation, we propose a solution based on multivariate linear modeling. We show that the approach provides a consistent and reliable method to compare the effectiveness of non-deterministic IR algorithms, and explain how statistics can safely be used to show that two IR algorithms have equivalent effectiveness.