@InProceedings{han-sun:2016:COLING,
  author    = {Han, Xianpei  and  Sun, Le},
  title     = {Context-Sensitive Inference Rule Discovery: A Graph-Based Method},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2902--2911},
  abstract  = {Inference rule discovery aims to identify entailment relations between
	predicates, e.g., ‘X acquire Y --> X purchase Y’ and ‘X is author of Y
	--> X write Y’. Traditional methods dis-cover inference rules by computing
	distributional similarities between predicates, with each predicate is
	represented as one or more feature vectors of its instantiations. These
	methods, however, have two main drawbacks. Firstly, these methods are mostly
	context-insensitive, cannot accurately measure the similarity between two
	predicates in a specific context. Secondly, traditional methods usually model
	predicates independently, ignore the rich inter-dependencies between
	predicates. To address the above two issues, this pa-per proposes a graph-based
	method, which can discover inference rules by effectively modelling and
	exploiting both the context and the inter-dependencies between predicates.
	Specifically, we propose a graph-based representation—Predicate Graph, which
	can capture the semantic relevance between predicates using both the
	predicate-feature co-occurrence statistics and the inter-dependencies between
	predicates. Based on the predicate graph, we propose a context-sensitive random
	walk algorithm, which can learn con-text-specific predicate representations by
	distinguishing context-relevant information from context-irrelevant
	information. Experimental results show that our method significantly
	outperforms traditional inference rule discovery methods.},
  url       = {http://aclweb.org/anthology/C16-1273}
}