Relational Learning With Expert Advice
Text Analysis Conference Workshop
The introduction of the paper, in lieu of an abstract: We present the Wisconsin-Indiana system for the TAC KBP 2015 slot filling task. The proposed approach is a three-staged pipeline. The first stage consists of converting the raw text to a large set of rich relational features (we employ first-order logic for representing the underlying text). During the second stage, weak supervised examples are generated using expert human advice that form weak annotations for the text. In the final stage, the relational features and weak labels are provided as input to an efficient learning system  that learns a Relational Dependency Network  from the given data.
Text Analysis Conference Workshop (TAC 2015)
November 16-17, 2015
D. Viswanathan, A. Wazalwar, Ameet Soni, J. W. Shavlik, and S. Natarajan.
"Relational Learning With Expert Advice".
Text Analysis Conference Workshop.