# Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning
## 2 Related Work  
There are three main categories of methods for event extraction: pattern-based methods, traditional machine learning methods, and deep learning methods. Pattern-based methods [6; 7; 8; 9; 10] are suitable for specific text formats in a single domain but heavily rely on manually crafted rules. Traditional machine learning methods [11; 12; 13; 14] have good portability and work effectively on simple texts but suffer from insufficient feature mining. Deep learning methods are the current mainstream paradigm for event extraction tasks, which focus on automatically mining data features through neural networks and accomplishing tasks through feature learning. Within the realm of deep learning methods, the initial approach was the pipeline model [15; 16; 17; 18; 19; 20; 21; 22], which divided event extraction into two sub-tasks: trigger word extraction and event argument extraction. [PERSON] et al. [15] proposed the Dynamic Multi-Pooling Convolutional Neural Network (DMCNN), which dynamically retained lexical features using convolutional neural networks and performed multi-classification tasks for the information extraction required in both stages. [PERSON] [16] combined Bi-LSTM and CRF to achieve good results by defining event extraction as a sequence labelling task. [PERSON] [18] proposed Multi-Turn Question Answering (MQAE), modelling the task as a series of machine reading comprehension models based on question-answer templates to address the problem of traditional classification-based extraction models being unable to generalize to new event types and argument roles. In general, the pipeline model is simple and intuitive, with good scalability and interpretability. However, it needs help with issues such as error accumulation and the loss of contextual information. To address these drawbacks, some researchers had proposed joint model methods [13; 23; 24; 25; 26; 27; 28; 29], which make full use of the interaction information of trigger words and event arguments and extract them simultaneously on this basis. [PERSON] et al. [13] implemented the joint learning of trigger words and event argument extraction tasks based on traditional feature extraction methods and obtained good results with a structured perceptron model. [PERSON] [23] constructed local features for text sequences and local windows, and global features for between-event trigger words, between-event arguments, and between trigger words and event arguments based on bidirectional recurrent neural networks through deep and joint learning, thus improving the performance of the event extraction task. [PERSON] et al. [27] defined information extraction as a generative model from sequences to structures, and proposed the Text2 Event model as an example for the event extraction task, which is able to learn a parallel corpus containing knowledge structures directly, thus modeling all sub-tasks of event extraction uniformly. Compared to the pipeline model, the joint model methods can effectively utilize contextual information and model the interactions among multiple sub-tasks to correct errors in individual sub-tasks. However, the models above only improved the model from the perspectives of model structure and problem definition, overlooking the prior knowledge learned by pre-trained language models. In recent years, prompt learning techniques have emerged as a new paradigm, combining the original input text with prompt templates to allow the model to output answersbased on prompt information. Prompt templates help the model learn the prior knowledge of pre-trained language models. Prompt learning has shown good performance in basic tasks such as text classification and sentiment recognition; however, this technique still lacks an effective application in the event extraction task. Additionally, acquiring high-quality annotated event corpora still poses challenges due to high costs and the lengthy annotation time.