Learning Commonsense Knowledge Models for Semantic Analytics

Commonsense knowledge is considered the basis for machine understanding of natural language semantics. In this paper, we propose a new method for fine-grained commonsense knowledge extraction using dependency graphs built through an unsupervised method. We implement the method on a large-scale text collection, and show how our knowledge model can be applied to develop a generic pronoun resolution algorithm. In particular, we demonstrate that our method leads to significant improvements in identifying false co-referred mention pairs, and hence could boost the precision of even the state of the art methods. Our method is unsupervised, and thus the performance can be further improved with bigger collections of raw texts.