As document collections grow larger, the information needs and relevance judgments in a test collection must be well-chosen within a limited budget to give the most reliable and robust evaluation results. In this work we analyze a sample of queries categorized by length and corpus-appropriateness to determine the right proportion needed to distinguish between systems. We also analyze the appropriate division of labor between developing topics and making relevance judgments, and show that only a small, biased sample of queries with sparse judgments is needed to produce the same results as a much larger sample of queries. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Carterette, B., Pavlu, V., Kanoulas, E., Aslam, J. A., & Allan, J. (2009). Investigating Learning Approaches for Blog Post Opinion Retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5478, pp. 313–324). Springer Verlag. https://doi.org/10.1007/978-3-642-00958-7_29
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