a system that uses structure event tuples input and standard neural network prediction model.

-   ***EB-CNN***: Ding et al. [@ding2015deep] proposed a model using event embedding input and Convolutional Neural Network as prediction model.

-   ***KGEB-CNN***: Ding et al. [@ding2016knowledge] further incorporated an outside knowledge graph into the learning process for event embedding. The model structure is the same as EB-CNN.

Result and Discussion
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Shown in table \[tab:Result\], the results on the comparison between the models SVM and the rest of the models indicates that deep neural network model achieves better performance than the SVM model. Comparison between Bag-At-LSTM and At-LSTM demonstrates that sentence encoding with LSTM have slightly better result than Bag-of-words model. Furthermore, WEB-At-LSTM and At-LSTM indicates that the character level composition helps improved the models accuracy. The technical Indicator leveraged from Zhai et al. [@zhai2007combining] doesn’t show any performance improvement to our model. In contrast, it results in a decline on the accuracy and it might be caused by adding noise to the dense representation vector $V$ before the output and prediction layer. The comparison between CNN-LSTM and At-LSTM shows that the news level self-attention layer can help capture more relevant news titles and their temporal 