This paper studies cold start Cumulative Citation Recommen dation (CCR) for Knowledge Base Acceleration (KBA), whose objective is to detect potential citations for target entities without existing KB entries from a volume of stream documents. Unlike routine CCR, in which target entities are identified by a reference KB, cold start CCR is more common since lots of less popular entities do not have any KB entry in practice. We propose a two-step strategy to address this problem: (1) event-based sentence clustering and (2) document ranking. In addition, to build effective rankers, we develop three kinds of features based on the clustering results: time range, local profile and action pattern. Empirical studies on TREC-KBA-2014 dataset demonstrate the effectiveness of the proposed strategy and the novel features.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper studies cold start Cumulative Citation Recommen dation (CCR) for Knowledge Base Acceleration (KBA), whose objective is to detect potential citations for target entities without existing KB entries from a volume of stream documents. 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