Reading the web with learned syntactic-semantic inference rules

Authors:
Ni Lao;Amarnag Subramanya;Fernando Pereira;William W. Cohen
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Google Research, Mountain View, CA;Google Research, Mountain View, CA;Carnegie Mellon University, Pittsburgh, PA
Venue:
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Year:
2012

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Abstract

We study how to extend a large knowledge base (Freebase) by reading relational information from a large Web text corpus. Previous studies on extracting relational knowledge from text show the potential of syntactic patterns for extraction, but they do not exploit background knowledge of other relations in the knowledge base. We describe a distributed, Web-scale implementation of a path-constrained random walk model that learns syntactic-semantic inference rules for binary relations from a graph representation of the parsed text and the knowledge base. Experiments show significant accuracy improvements in binary relation prediction over methods that consider only text, or only the existing knowledge base.