Improving Test Collection Pools with Machine Learning.

IR experiments typically use test collections for evaluation. Such test collections are formed by judging a pool of documents retrieved by a combination of automatic and manual runs for each topic. The proportion of relevant documents found for each topic depends on the diversity across each of the runs submitted and the depth to which runs are assessed (pool depth). Manual runs are commonly believed to reduce bias in test collections when evaluating new IR systems. In this work, we explore alternative approaches to improving test collection reliability. Using fully automated approaches, we are able to recognise a large portion of relevant documents that would normally only be found through manual runs. Our approach combines simple fusion methods with machine learning. The approach demonstrates the potential to find many more relevant documents than are found using traditional pooling approaches. Our initial results are promising and can be extended in future studies to help test collection curators ensure proper judgment coverage is maintained across the entire document collection.