Study on Text Component Recognition of Narrative Texts Based on Prompt Learning

Text structure analysis is one of the important techniques in automated essay scoring and an important research topic in the field of natural language processing.In recent years,research on the analysis of essay structure has been scarce and mainly focused on argumentative essays.There are still shortcomings in the study of narrative texts,especially in terms of research me-thods and resources,which are relatively limited.In response to these issues,this paper constructs a corpus for identifying the components of narrative texts in primary and secondary schools.A corpus automatic annotation model based on BERT-BiLSTM is used to improve annotation efficiency,and statistical analysis is conducted on content distribution and consistency of corpus annotation.This paper proposes a narrative text component recognition method based on prompt learning,which automatically constructs prefix prompt templates for recognizing text components and utilizes hierarchical attention mechanism to learn richer text features,thereby improving the ability to recognize narrative text structure.Experiments are conducted on a self-built dataset,and the results show that the proposed method improves the accuracy of narrative discourse structure to 85.80%,which is superior to the pre-trained language models used for comparison.