Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6): 330-335.doi: 10.11896/jsjkx.240400043

• Artificial Intelligence • Previous Articles     Next Articles

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

WANG Xiaoyi1, WANG Jiong2, LIU Jie1,3, ZHOU Jianshe1   

  1. 1 China Language Intelligence Research Center,Capital Normal University,Beijing 100048,China
    2 School of Information Engineering,Capital Normal University,Beijing 100048,China
    3 School of Information Technology,North China University of Technology,Beijing 100144,China
  • Received:2024-04-07 Revised:2024-09-12 Online:2025-06-15 Published:2025-06-11
  • About author:WANG Xiaoyi,born in 1997,Ph.D.Her main research interests include natural language processing and automated essay scoring.
    LIU Jie,born in 1970,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.10359S).His main research in-terests include natural language processing and knowledge engineering.
  • Supported by:
    National Key Research and Development Program of China(2020AAA0109703) and National Natural Science Foundation of China(62076167,U23B2029).

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52

Abstract: 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.

Key words: Dataset construction, Text structure, Automated essay scoring, Prompt learning

CLC Number: 

  • TP391
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