Abstract. The Slot Filling task requires a system to automatically distill information from a large document collection and return answers for a query entity with specified attributes (‘slots’), and use them to expand the Wikipedia infoboxes. We describe two bottom-up Information Extraction style pipelines and a top-down Question Answering style pipeline to address this task. We propose several novel approaches to enhance these pipelines, including statistical answer re-ranking and Markov Logic Networks based cross-slot reasoning. We demonstrate that our system achieves state-of-the-art performance, with 3.1 % higher precision and 2.6 % higher recall compared with the best system in the KBP2009 evaluation.

The CUNY-BLENDER team participated in the following tasks in TAC-KBP2010: Regular