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In this paper we describe the University of Amsterdam's approach to the TREC 2013 Knowledge Base Acceleration (KBA) Cumulative Citation Recommendation (CCR) track. The task is to filter a stream of documents for documents relevant to a given set of entities. We model the task as a multi-class classification task. Entities may evolve over time and the classifier should be able to adapt to these changes at runtime. To achieve this, the classifier performs online self-learning, i.e., learning onlydblp:conf/trec/Kenter13 fatcat:552epmznwzdtlcavg6qlnmnwyq