Research on Document Level Event Argument Extraction Method Based on Prompt Learning
Event argument extraction refers to the recognition of event arguments and their corresponding roles in natural language texts,which is the key to event extraction.The traditional event argument extraction method limits the extraction scope to a single sentence and performs poorly when faced with scattered arguments in long texts.In recent years,researchers have proposed a discourse level event argument extraction method based on prompt learning,which can obtain event arguments from input text based on prompt information and achieve event argument extraction.However,most existing methods based on prompt learning rely on manual construction of prompt tem-plates,and fixed template structures can easily lead to argument extraction errors.In response to the above shortcomings,we propose a method for automatically constructing templates based on text trigger words based on previous research on prompt learning,and integrate event role semantic information into the input text,enabling the model to better capture text semantic features and improve the accuracy of event argument extraction.The experimental results on the discourse level dataset RAMS show that the F1 values of the proposed model inevent argument recognition and event argument classification reach 54.3% and 48.1%,respectively,which are 1.8 and 1.2 percentage points higher than the optimal benchmark method,respectively,verifying the effectiveness of the model.
argument extractionprompt learningtrigger wordsspan selectorpretrained language model
万方数据
维普
