Most work on learning entailment rules between predicates considered each rule independently of others, using two sources of information: lexicographic resources and distributional similarity .

Distributional similarity algorithms use large corpora to learn broader resources by assuming that semantically similar predicates appear with similar arguments.

Distributional similarity algorithms differ in their feature representation: Some use a binary representation: each predicate is represented by one feature vector where each feature is a pair of arguments (Szpektor et al., 2004; Yates and Etzioni, 2009).

Second, to distributional similarity algorithms: (a) SR: the score used by Schoenmackers et al.

Third, we compared to the entailment classifier with no transitivity constraints (clsf) to see if combining distributional similarity scores improves performance over single measures.

We compute 11 distributional similarity scores for each pair of predicates based on the arguments appearing in the extracted arguments.

Finally, we present a ranker that employs distributional similarities to build a network of words, and captures the diversity of perspectives by detecting communities in this network.

Finally, we proposed a ranking system that employs word distributional similarities to identify semantically equivalent words, and compared it with a wide

5.1 Distributional Similarity

In order to capture the nuggets of equivalent semantic classes, we use a distributional similarity of

The method based on the distributional similarities of words outperforms other methods in the citations category.