# A Survey of Information Extraction Based on Deep Learning
## 3 Event Extraction Based on Deep Learning
### Single-Task or Multitask Event Extraction
When entering the private network of each task, the shared representation and the private representation are spliced for each task. From the perspective of multi-task applications, the application of multi-task learning to domain migration scenarios has received considerable attention in recent years. [PERSON] et al. proposed a multi-task model, with named entity recognition as the primary task and sentence classification as an auxiliary task, and improved the performance of the low-resource NER model by jointly training the NER and sentence classification models [75]. Most existing entity link tasks focus only on entity disambiguation while ignoring entity recognition. [PERSON] and others jointly learned named entity recognition and entity links and added an attention mechanism to Stack-LSTM. Each decision uses the information from the two tasks to obtain a more robust system [76]. [PERSON] et al. first annotated triggers and event elements jointly. To avoid multiple triggers for a sentence, the model trained an extraction model for each event type. Finally, to solve the problem of data sparsity, multi-task learning sharing task information was proposed [77]. To solve the problem of low-resource language sequence annotation, as an example, [PERSON] et al. proposed a multi-language and multi-task framework, the infrastructure of which comprises CNN encoding character information and an LSTM encoding context information using the conditional  
Figure 5: The DMCNN uses dynamic multi-pooling convolution to capture different parts of a sentence, thereby retaining more critical information [71]. Figure 6: The architecture of JRNN, which reduces the propagation of 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]. random field (CRF) model to obtain the optimal sequence labels. This method shared the coding and linear layers and the CRF layer [78]. [PERSON] et al. used the correlation between the two and proposed the MDL-J3E joint extraction model based on multi-task learning, which is essentially a shared-private primary-auxiliary multi-task learning network model that regards event detection as the main task and named entity recognition as an auxiliary task [74]. An empirical study on the ACE2005 data set showed that the proposed model achieved 84.15% on the named entity recognition task for F1 and 70.96% for the event detection task (The results are shown in Table 3). Compared with single-task learning, multi-task learning can fully alleviate the problem of sparse data, solve the problem of the existing event extraction model by assuming that entities exist or ignoring entity information, and provide a new idea for the event extraction task. [PERSON] et al. proposed a novel named entity recognition model framework containing shared-private domain parameters and multi-task learning applied to multi-domain and domain labels not encountered in training [79]. The model adopted the basic framework of BiLSTM-CRF and modelled the feature mapping of shared and private features based on this framework. Private modules were used when domain labels and common modules' domain labels were unknown. In addition, domain recognition was also included as an auxiliary task for named entity recognition tasks to help the model further improve its performance. In addition to domain-transfer tasks, multi-task learning is widely used in areas, such as law, medical care, and emotion analysis. In addition, [PERSON] and others creatively proposed a unified text-to-structure generation framework named universal information extraction (UIE), which can model different IE tasks in general, generate target structures adaptively, and learn common IE capabilities from different knowledge sources (as shown in Figure 7). The experimental results show that UIE has achieved very competitive performance in both supervised and low-resource environments, which verifies its versatility, effectiveness, and portability [80].