Abstract:  Event extraction is an important research focus in information extraction， which aims to extract event structured information from text by identifying and classifying event triggers and arguments. Traditional methods rely on complex downstream networks， require sufficient training data， and perform poorly in situations where data is scarce. Existing research has achieved certain results in event extraction using prompt learning， but it relies on manually constructed prompts and only relies on the existing knowledge of pre-trained language models， lacking event specific knowledge. Therefore， a knowledge based fine-tuning event extraction method is proposed. This method adopts a conditional generation approach， injecting event information to provide argument relationship constraints based on existing pre-trained language model knowledge， and optimizing prompts using a fine-tuning strategy. Numerous experiment results show that compared to traditional baseline methods， this method outperforms the baseline method in terms of trigger word extraction and achieves the best results in small samples.

Key words:  , event extraction； prompt learning； information extraction； natural language processing； pre-trained language model