Entailment learning Two information types have primarily been utilized to learn entailment rules between predicates: lexicographic resources and distributional similarity resources.

Therefore, distributional similarity is used to learn broad-scale resources.

Distributional similarity algorithms predict a semantic relation between two predicates by comparing the arguments with which they occur.

When computing distributional similarity scores, a template is represented as a feature vector of the CUIs that instantiate its arguments.

Local algorithms We described 12 distributional similarity measures computed over our corpus (Section 5.1).

For each distributional similarity measure (altogether 16 measures), we learned a graph by inserting any edge (u, v) , when u is in the top K templates most similar to 2).

Next, we represent each pair of propositional templates with a feature vector of various distributional similarity scores.

Distributional similarity representation We aim to train a classifier that for an input template pair (t1, t2) determines whether t1 entails 752.

A template pair is represented by a feature vector where each coordinate is a different distributional similarity score.

We consider three similarity data sources: the Moby thesaurus1 , WordNet (Fellbaum, 1998), and distributional similarity based on a large corpus of text (Lin, 1998).

Distributional similarity .

Second, the data sources used: each source separately (M for Moby, W for WordNet, D for distributional similarity ), and all three in combination (MWD).