AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

Font Size: 
What Happens Next? Future Subevent Prediction Using Contextual Hierarchical LSTM
Linmei Hu, Juanzi Li, Liqiang Nie, Xiao-Li Li, Chao Shao

Last modified: 2017-02-12

Abstract


Events are typically composed of a sequence of subevents. Predicting a future subevent of an event is of great importance for many real-world applications. Most previous work on event prediction relied on hand-crafted features and can only predict events that already exist in the training data. In this paper, we develop an end-to-end model which directly takes the texts describing previous subevents as input and automatically generates a short text describing a possible future subevent. Our model captures the two-level sequential structure of a subevent sequence, namely, the word sequence for each subevent and the temporal order of subevents. In addition, our model incorporates the topics of the past subevents to make context-aware prediction of future subevents. Extensive experiments on a real-world dataset demonstrate the superiority of our model over several state-of-the-art methods.

Keywords


event prediction; LSTM; subevent sequence

Full Text: PDF