Paper: Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues

ACL ID D13-1080
Title Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Automatically constructed Knowledge Bases (KBs) are often incomplete and there is a gen- uine need to improve their coverage. Path Ranking Algorithm (PRA) is a recently pro- posed method which aims to improve KB cov- erage by performing inference directly over the KB graph. For the first time, we demon- strate that addition of edges labeled with la- tent features mined from a large dependency parsed corpus of 500 million Web documents can significantly outperform previous PRA- based approaches on the KB inference task. We present extensive experimental results val- idating this finding. The resources presented in this paper are publicly available.