News event prediction by trigger evolution graph and event segment

Event prediction aims to predict the most possible following event given a chain of closely related context events. Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information. Current datasets for event prediction, naturally, can be used for supervised learning. Event chains are either from document-level procedural action flow, or from news sequences under the same column. This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus, and adopts the standard multiple choice narrative cloze task evaluation. The topic model is utilized to extract event chains from the news corpus to deal with training data bottleneck. Based on trigger-guided structural relations in the event chains, we construct trigger evolution graph, and trigger representations are learned through graph convolutional neural network and the novel neighbor selection strategy. Then there are features of two levels for each event, namely, text level semantic feature and trigger level structural feature. We design the attention mechanism to learn the features of event segments derived in term of event major subjects, and integrate relevance between event segments and the candidate event. The most possible next event is picked by the relevance. Experimental results on the real-world news corpus verify the effectiveness of the proposed model.