ERDAP: A Novel Method of Event Relation Data Augmentation Based on Relation Prediction

Abstract

Event relation extraction is a key aspect in the fields of event evolutionary graph construction, knowledge question and answer, and intelligence analysis, etc. Currently, supervised learning methods that rely on large amounts of labeled data are mostly used; however, the size of existing event relation datasets is small and cannot provide sufficient training data for the models. To alleviate this challenging research question, this study proposes a novel data augmentation model, called Event Relation Data Augmentation based on relationship Prediction (ERDAP), that allows both semantic and structural features to be taken into account without changing the semantic relation label compatibility, uses event relation graph convolutional neural networks to predict event relations, and expands the generated high-quality event relation triples as new training data for the event relation texts. Experimental evaluation using event causality extraction method on Chinese emergent event dataset shows that our model significantly outperforms existing text augmentation methods and achieves desirable performance, which provides a new idea for event relation data augmentation