Abstract: This paper proposes an automatic event semantic division algorithm based on instance distribution constraints to address the difficulty in aggregating event semantics that are discretely distributed in news text collections. First, the distant supervision method is used to construct training dataset for event semantic division. Second, a semantic classifier based on instance constraints is designed to determine whether the addition of new event trigger affects the aggregation of event semantics. Finally, an event semantic set generation algorithm is designed based on the classifier, which can automatically divide the discrete event triggers into different event semantic sets without the need for pre-setting event types. Experimental results show that the proposed method can effectively classify event semantics, and offer a new approach for achieving high-quality aggregation of event semantics.

Key words: instance distribution constraint, automatic event semantic division, distant supervision, event semantic classifier, set generation algorithm