  Computational and cognitive studies suggest that the abstraction of
eventualities (activities, states, and events) is crucial for humans to
understand daily eventualities. In this paper, we propose a scalable approach
to model the entailment relations between eventualities ("eat an apple''
entails ''eat fruit''). As a result, we construct a large-scale eventuality
entailment graph (EEG), which has 10 million eventuality nodes and 103 million
entailment edges. Detailed experiments and analysis demonstrate the
effectiveness of the proposed approach and quality of the resulting knowledge
graph. Our datasets and code are available at
https://github.com/HKUST-KnowComp/ASER-EEG.

Computation and Language