University of Texas at Austin KBP 2013 Slot Filling System: Bayesian Logic Programs for Textual Inference (2013)
This document describes the University of Texas at Austin 2013 system for the Knowledge Base Population (KBP) English Slot Filling (SF) task. The UT Austin system builds upon the output of an existing relation extractor by augmenting relations that are explicitly stated in the text with ones that are inferred from the stated relations using probabilistic rules that encode commonsense world knowledge. Such rules are learned from linked open data and are encoded in the form of Bayesian Logic Programs (BLPs), a statistical relational learning framework based on directed graphical models. In this document, we describe our methods for learning these rules, estimating their associated weights, and performing probabilistic and logical inference to infer unseen relations. In the KBP SF task, our system was able to infer several unextracted relations, but its performance was limited by the base level extractor.
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In Proceedings of the Sixth Text Analysis Conference (TAC 2013) 2013.
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Yinon Bentor Ph.D. Student yinon [at] cs utexas edu
Shruti Bhosale Formerly affiliated Masters Student shruti [at] cs utexas edu
Amelia Harrison Formerly affiliated Ph.D. Student ameliaj [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu