# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 4 Proposed Method
### Candidate Entity Extraction  
Candidate entity extraction, as the first subtask of the DEE task, has a huge impact on the performance of the two subsequent subtasks of entity type detection and argument identification. However, in previous candidate entity extraction tasks, candidate entities are usually considered as flat entities and the candidate entity extraction task is accomplished using sequence annotation. Although this sequence labeling-based approach  
Figure 7: Overall model structure of RDEE. achieves better results in the task of flat entity extraction, it fails to address the issue of overlapping roles and cannot accurately extract entities that have multiple roles. To solve this problem, we firstly use two different matrices to represent the head position information and tail position information of candidate entities respectively in this stage. We then employ multiplicative attention to facilitate interaction between the two matrices, enabling the extraction of deep information. This process yields a score matrix, which is subsequently used to accomplish candidate entity extraction. Specifically, given a document \(D=\{S_{i}\}_{i=1}^{N_{i}}\), where each sentence is represented as \(S_{i}=\left[x_{i}^{1},x_{i}^{2},\ldots,x_{i}^{m}\right]\), we first apply embedding using the pre-trained language model RoBERTa to obtain an embedded representation \(S_{i}=\left[w_{i}^{1},w_{i}^{2},\ldots,w_{i}^{m}\right]\), where \(m\) represents the sentence length. Then, to obtain the textual representation, we encode the embedded representation of the text using the Transformer encoder, and eventually we can obtain the text representation \(H_{i}\) for each sentence \(S_{i}\) as follows:  
\[\left[w_{i}^{1},w_{i}^{2},\ldots,w_{i}^{m}\right]=RoBERTa\left(\left[x_{i}^{1},x_{i}^{2},\ldots,x_{i}^{m}\right]\right) \tag{1}\]  
\[\left[h_{i}^{1},h_{i}^{2},\ldots,h_{i}^{m}\right]=Transformer\left(\left[w_{ i}^{1},w_{i}^{2},\ldots,w_{i}^{m}\right]\right) \tag{2}\]  
where \(w_{i}^{j}\in R^{d}\), \(h_{i}^{j}\in R^{d}\), and \(d\) are the hidden layer sizes. Finally, in order to accurately extract the overlapping candidate entities of characters, we use two FNNs networks to generate two different matrices: \(H_{s}\) and \(H_{e}\). These matrices represent the head position information and tail position information of candidate entities, capturing the contextual information of the target characters. By using different matrices to represent the head position information and tail position information of the candidate entities and training them, the start position and end position of the candidate entities can be identified, respectively. Since the contexts of the start and end positions of the candidate entities are different, using different matrices to represent the head position information and tail position information of the candidate entities, respectively, greatly improves task accuracy compared with using the output of Transformer directly. We employ multiplicative attention, as illustrated in Figure 8, to facilitate the interaction between the start position information and end position information. This interaction produces a score matrix, denoted as \(C_{i}\in R^{head\times m\times m}\), for candidate entity prediction, where _head_ is the number of predefined role types plus one (i.e., non-predefined role types). We obtain the score matrix in the following way:  
\[H_{s}=\text{FFNNs}\left(\left[h_{i}^{1},h_{i}^{2},\ldots,h_{i}^{m}\right]\right) \tag{3}\]  
\[H_{e}=\text{FFNNs}\left(\left[h_{i}^{1},h_{i}^{2},\ldots,h_{i}^{m}\right]\right) \tag{4}\]  
\[C_{i}=h_{s,begin,r_{u}}h_{s,end,r_{u}}^{T} \tag{5}\]  
where \(H_{s}\in R^{m\times head\times o}\), \(H_{e}\in R^{m\times head\times o}\), and \(o\) are hyperparameters, indicating the window size used to obtain the contextual embedding of the target character. In this case, we set its value to 64, indicating that 64 characters preceding and following the target character are obtained to form the contextual representation.