# A Survey of Information Extraction Based on Deep Learning
## 3 Event Extraction Based on Deep Learning
### Sentence-Level or Chapter-Level Event Extraction  
Generally, for simple events, types of events can be directly identified from a sentence and the required metadata can be extracted. However, with the escalating complexity of the event, sentence-level extraction cannot cover all the metadata of the event, and complete information needs to be extracted from multiple sentences. Therefore, the event extraction task can be divided into sentence-level and chapter-level extractions from the learning scope. The research task of event extraction was initially based mainly on sentence-level extraction of ACE2005 data. Gradually, the information obtained from the extraction of the isolated sentences was likely incomplete. For sentence-level extraction, [PERSON] et al. combined a mixed neural network with a BiLSTM model and a CNN model to conduct event extraction by modeling the sequence of  
Figure 7: From (**a**) task-specialized IE: different tasks, different structures, different schemas to (**b**) universal IE: unified modeling via structure generation [80]. input sentences and obtaining semantic information of the structure and sequence from a specific context [84]. [PERSON] et al. introduced a new event-triggered word extraction model, CNN-BiGRU, which extracted word-level features through CNN, then captured text semantic information through BiGRU to obtain sentence-level features, and finally spliced word-level and sentence-level features to achieve the identification of trigger words and predict event categories [85]. [PERSON] et al. proposed a joint method for extracting entities and events in sentences using the HNN-EE model [86]. The model uses the BiLSTM model to identify entities, and then transmits the context information acquired on the BiLSTM to the self-attention layer and gating convolutional layer of the neural network for event extraction, as shown in Figure 8.  
For document-level event extraction, the entire document is directed as the input, and then the desired event elements are the outputs. Chapter-level event extraction requires new ideas. In a document that describes an event, an event-centric sentence often expresses the event best. Most of the information is often covered in the event center sentence; therefore, [PERSON] et al. constructed a DCFEE framework for document event extraction according to the above idea. The specific architecture of the framework is shown in Figure 9. The framework was designed to divide the event-extraction process into two parts. First, using sentence-level extraction, the BiLSTM-CRF sequence annotation model was used for the input sentences to obtain the trigger words and arguments of the sentence. The second part performed document-level extraction. If the sentence was judged as being central to the output of the previous step, the arguments were extracted in the context of a sentence with full event information [87]. [PERSON] et al. elucidated the differences between sentence-level event extraction and document-level event extraction, and jointly extracted sentence-level entities based on the attention mechanism sequence annotation model. Then,  
Figure 8: The architecture of HNN-EE, which uses the BiLSTM model to identify entities, and then transmits the context information acquired on the BiLSTM to the self-attention layer and gating convolutional layer of the neural network for event extraction [86]. based on sentence-level event extraction, sentence-level information was integrated using integer linear programming to obtain document-level event information [88]. [PERSON] et al. proposed a novel end-to-end event extraction model for the financial domain [89]. The main idea of the model was to convert document-level event table filling (DEE) into an entity-based directed acyclic graph (EDAG), which can be divided into four steps: entity recognition, document-level information coding, EDAG generation, and EDAG path expansion.