needed to identify an event drawn from the set [@Cross]. Cross entropy is the most suitable loss function since we want to measure the output from our prediction model with it’s true output. A binary cross entropy loss formula are shown in Equation (7). In which, $J(w)$ refers to the loss, $N$ refers to the number of examples, $y_n$ is the expected output and $\tilde{y}_n$ is true output. We use this formula as our loss function in the model. $$\begin{aligned}
        J(w)=-\frac{1}{N}H(p_n,q_n)=-\frac{1}{N}\displaystyle\sum_{i=1}^{N} [y_n*\log\tilde{y}_n+(1-y_n)*\log(1-\tilde{y}_n)]\end{aligned}$$

Model design
------------

In this subsection, we introduce the design of our Attention-based LSTM model (At-LSTM). The design has four stages: Input and Embedding layer, news-level Bi-LSTM and self-attention layer, day-level Bi-LSTM and self-attention layer and the final output and prediction layer. These stages will be described below and shown in figure  \[fig:model\].

![\[fig:model\]Model Structure](Model.png){width="100.00000%" height="3.5in"}

### Input and Embedding Layer

***Model Input***: The same world event can be described in different expression ways. This variety makes the representation space for events very sparse. Several approaches [@ding2015deep; @ding2016knowledge] represent the event as tuple $< S; V; O >$ to gain generalization. However, we argue that this representation is oversimplified and might lose lots of valuable information. Instead, we 