Stanford's Distantly Supervised Slot Filling Systems for KBP 2014

We describe Stanford’s entry in the TACKBP 2014 Slot Filling challenge. We submitted two broad approaches to Slot Filling, both strongly based on the ideas of distant supervision: one built on the DeepDive framework (Niu et al., 2012), and another based on the multi-instance multilabel relation extractor of Surdeanu et al. (2012). In addition, we evaluate the impact of learned and hard-coded patterns on performance for slot filling, and the impact of the partial annotations described in Angeli et al. (2014).

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