# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 1 Introduction
Subsequently, a convolutional neural network is employed to classify each sentence and determine whether it qualifies as a key sentence. Additionally, to acquire comprehensive event arguments, an argu  
Figure 1: Event extraction instance. Among them, “12 \(\lambda\)” corresponds to “12 people”; “\(\
exists\  
+++ ==WARNING: Truncated because of repetitions==
)\(\  
++  
ment complementation strategy is proposed to obtain arguments from the surrounding sentences of the key event's location for supplementation. [PERSON] et al. [9] redesigned the DEE task to treat the DEE task as a table-filling task using a trigger-word-free approach to populate candidate entities into a predefined event table. Specifically, they modeled DEE as a continuous prediction paradigm in which arguments are predicted in a predefined role order and multiple events are also extracted in a predefined event order. While the method achieves the goal of the DEE task through trigger-word-free extraction, it introduces error propagation issues as the former argument identification results do not consider the latter argument identification results, given that arguments are predicted in a predefined role order during the argument identification process. [PERSON] et al. [10] proposed an end-to-end model in which information about multiple events as well as event arguments is extracted simultaneously from the document in a parallel manner using a multi-grain decoder after the overall document representation is obtained using multiple encoders. Based on previous work, we can divide the DEE task into three subtasks: candidate entity extraction, event type detection, and argument identification. Among them, candidate entity extraction involves extracting entities related to events from the text; event type detection involves determining the types of events present in the text; and argument identification involves identifying the arguments belonging to an event among the candidate entities. Candidate entity extraction, as the first subtask of DEE, affects the effectiveness of the two subsequent subtasks. Previous research has focused on addressing issues such as scattered arguments and multiple events, overlooking the presence of role overlapping in the first subtask. However, this overlapping significantly impacts the performance of both subsequent subtasks and the overall DEE task. Role overlapping refers to the phenomenon of candidate entities playing multiple roles in the same event or in multiple different events. For example, in Figure 1, the entity \"12 \(\bigtriangleup\)\"(12 people) plays the role of \"InjureDead\" and \"DeadPerson\" in the event of \"InjureDead\"; the entity \"(Chongqing Yongchuan Diaoshuidong coal mine) plays the role of \"RescurePlace\" in the \"Rescure\" event and \"HappenPlace\" role in the \"Accident\" event. To address the above-mentioned problems of missing datasets and role overlapping, we have done the following two things. On the one hand, to address the lack of datasets, we defined a framework for unexpected event extraction by analyzing and summarizing information of Chinese emergency events, and constructed a Chinese document-level emergency event extraction dataset CDEEE. We defined 4 event types and 19 role types in this dataset. Furthermore, we have provided annotations for three specific issues: argument scattering, multiple events, and role overlapping. Ultimately, the CDEEE dataset has been annotated, consisting of 5000 documents and 10,755 events. On the other hand, to address the role overlapping problem, we propose the DEE model RODEE. In this model, we first use the pre-trained language model RoBERTa-wwm [11] to embed the text representation and then encode it using Transformer to obtain the text representation, which gives us an overall understanding of the text.