计算机工程2025，Vol.51Issue(9)：120-128,9.DOI:10.19678/j.issn.1000-3428.0069414
基于层级软提示交互融合的少样本事件方面类别检测方法
Method for Event Aspect Category Detection in Few Shot Scenarios via Hierarchical Soft Prompt Interaction Fusion
摘要
Abstract
Event Aspect Category Detection(ACD)aims to identify the aspect categories present in event text.Data need to be collected from various fields and textual events,particularly when researching public opinion on social media.The first phase of social media opinion events lacks sufficient data for labeling event text.The pressing issue is precisely detecting event aspects using a limited amount of labeled data.This paper presents a novel method for event ACD with limited samples.This method utilizes a pre-trained model to construct soft prompt templates,performs hierarchical semantic characterization and interaction fusion,and adaptively combines multilayer prompt characterizations.The objective is to enhance the accuracy of event ACD using limited samples.Experiments on a self-constructed Chinese social media dataset and English dataset demonstrate that the proposed method is significantly superior to other baseline methods.Further ablation experiments and visualizations confirm the effectiveness of the proposed multilayer prompt interaction fusion module.关键词
少样本/提示学习/软提示/方面类别检测/多层级提示交互融合Key words
few-shot/prompt learning/soft prompt/Aspect Category Detection(ACD)/multilayer prompt interaction fusion分类
信息技术与安全科学引用本文复制引用
艾传鲜,郭军军,尹兆良..基于层级软提示交互融合的少样本事件方面类别检测方法[J].计算机工程,2025,51(9):120-128,9.基金项目
国家重点研发计划(202301AT070444) （202301AT070444）
国家自然科学基金(62366025) （62366025）
云南省科技厅自然科学基金(202202AE090008-3). （202202AE090008-3）
