Stanford’s 2014 Slot Filling Systems
نویسندگان
چکیده
We describe Stanford’s entry in the TACKBP 2014 Slot Filling challenge. We submitted two broad approaches to Slot Filling: one based on the DeepDive framework (Niu et al., 2012), and another based on the multi-instance multi-label 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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