# A Survey of Information Extraction Based on Deep Learning
## 3 Event Extraction Based on Deep Learning
### Single-Task or Multitask Event Extraction  
Neural network event extraction is divided into single-task extraction, according to different model learning modes, and multi-task extraction. Usually, event extraction can be summarized into tasks, such as trigger word extraction and argument extraction. The single-task extraction model transforms the event extraction task into a multi-stage classification problem, and the extraction is completed sequentially or jointly. [PERSON] et al. proposed a dynamic multi-pooling convolutional neural network (DMCNN), as shown in Figure 5, which was designed to divide a sentence into multiple modules through sequence division and successively perform the tasks of trigger word extraction and argument extraction [71]. In the event extraction task, because a sentence may contain multiple events, different arguments play different roles in different event types, and the traditional convolutional neural network model using the maximum pooling in each sentence can capture the maximum multiple-event sentence event extraction models misinformation. The DMCNN uses dynamic multi-pooling convolution to capture different parts of a sentence, thereby retaining more critical information. In an event, the amount of argument information is far greater than that of the trigger word, and because the pipeline model is used in the extraction, the LIU joint model primarily solves the problem of argument recognition extraction. Through a series of experiments, it was found that the joint model for the detection of the argument prediction effect was significantly better than the effect of argument detection alone [72]. Since the meta-information in the sentence is more important than the non-meta-information, the study adopted the attention mechanism to improve the weight of the meta-information when triggering word detection, and the results achieved a good detection effect. As a representative work, [PERSON] et al. used a bi-directional recursive neural network (JRNN) for event extraction (as shown in Figure 6). This method reduces the propagation of the error by combining local and global features in the recurrent neural network and designing different memory matrices to enhance the connection of different events [73]. [PERSON] et al. compared their JRNN model with the CNN and DMCNN models mentioned above and found that this model significantly outperformed other models [71]. As shown in the following figure, the model is more accurate than the DMCNN model in trigger word recognition, further indicating that the JRNN benefits from a memory function. For the event extraction task, trigger word recognition is the basis of the task, and its results will have an impact on subsequent work; thus, the JRNN model achieves good results. In the single task of joint event extraction, considering the correlation of named entity identification and event detection, an increasing number of researchers have proposed joint learning-based event detection methods to extract events and entities simultaneously. Multi-task learning refers to a given machine learning model endowed with multiple sub-tasks. Each sub-task has a certain correlation and improves the learning ability of the model on multiple tasks by using the correlation between each sub-task. Compared with single-task models, multi-task models have shown better results in named entity recognition tasks and event detection tasks. From the perspective of the multi-task sharing mode, the existing multi-task learning model can be divided into three methods, including a hard sharing, soft sharing, and sharing-private modes [74]. The hard sharing mode uses the same representation shared between multiple tasks, with the private layer adapted to a specific task. The soft sharing mode indicates that after the sharing layer, the sharing layer output produces different ratios. For the same input, each task obtains different representations with a certain flexibility. The sharing-private mode uses a private representation of each task in addition to a shared representation.