OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
How reliable are the results of large-scale information retrieval experiments?
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Forming test collections with no system pooling
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Managing déjà vu: Collection building for the identification of nonidentical duplicate documents
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A boosting algorithm for learning bipartite ranking functions with partially labeled data
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A two-stage text mining model for information filtering
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Mining Negative Relevance Feedback for Information Filtering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mining positive and negative patterns for relevance feature discovery
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
A pattern mining approach for information filtering systems
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A two-stage decision model for information filtering
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MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Discovering relevant features for effective query formulation
IRFC'12 Proceedings of the 5th conference on Multidisciplinary Information Retrieval
Adopting relevance feature to learn personalized ontologies
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Scoring-Thresholding pattern based text classifier
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Matching Relevance Features with Ontological Concepts
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
A pattern based two-stage text classifier
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Web Intelligence and Agent Systems
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Test collections for the filtering track in TREC have typically used either past sets of relevance judgments, or categorized collections such as Reuters Corpus Volume 1 or OHSUMED, because filtering systems need relevance judgments during the experiment for training and adaptation. For TREC 2002, we constructed an entirely new set of search topics for the Reuters Corpus for measuring filtering systems. Our method for building the topics involved multiple iterations of feedback from assessors, and fusion of results from multiple search systems using different search algorithms. We also developed a second set of "inexpensive" topics based on categories in the document collection. We found that the initial judgments made for the experiment were sufficient; subsequent pooled judging changed system rankings very little. We also found that systems performed very differently on the category topics than on the assessor-built topics.