Combining Event Segment Classification And Graph Self-Encoder For Event Prediction

Abstract

Scripted event prediction is the automatic learning of knowledge from unstructured text to predict subsequent events based on a given sequence of events. Existing work has mainly been based on event pairs or entire event chains for learning, which may ignore much structural and semantic information. In order to learn global dependencies between nodes in event evolution graphs, this paper proposes an event prediction method combining event segment classification and graph self-encoder, which uses a variational self-encoder of the graph to obtain the global structure of the event graph, learn and represent potential evolution patterns between events, and construct a global representation of the event graph. The method combines different rules to classify event segments, learns the features of different event segments, and predicts events by determining the scores of candidate events based on the correlation between the event segments and the candidate events. Experiments on a standard dataset demonstrate the effectiveness of the method.