Cluster Hypothesis in Low-Cost IR Evaluation with Different Document Representations
Kai Hui, Klaus Berberich · 2016
Offline evaluation for information retrieval aims to compare the performance of retrieval systems based on relevance judgments for a set of test queries. Since manual judgments are expensive, selective labeling has been developed to semi-automatically label documents, in the wake of the similarity relationship among retrieved documents. Intuitively, the agreement w.r.t the cluster hypothesis can directly determine the amount of manual judgments that can be saved by creating labels with a semi-automatic method. Meanwhile, in representing documents, certain information is lost. We argue that better document representation can lead to better agreement with the cluster hypothesis. To this end, we investigate different document representations on established benchmarks in the context of low-cost evaluation, showing that different document representations vary in how well they capture document similarity relative to a query.