山西大学学报（自然科学版）2024，Vol.47Issue(3)：494-505,12.DOI:10.13451/j.sxu.ns.2024002
基于提示增强原型网络的小样本多标签方面类别检测
Few-shot Multi-label Aspect Category Detection Based on Prompt-Enhanced Prototypical Network
摘要
Abstract
Few-shot multi-label aspect category detection is a prominent area of research within fine-grained sentiment analysis.In methods based on prototypical networks,the lack of training data and the presence of irrelevant noise words severely impact the quality of prototype vector generation using attention mechanisms.Addressing this challenge,our study introduces the ProtPrompt model,an innovative prototype network enhanced through prompt learning.Firstly,we align pre-trained tasks with downstream tasks using prompt learning,guiding the model in precise sentence representation to learn more discriminative vectors.This effectively en-hances category distinguishability.Meanwhile,we utilize cosine similarity to calculate the loss function instead of the commonly used Euclidean distance in prototypical networks,mitigating the influence of high-dimensional vector spaces.Secondly,we design an optimized framework to attenuate noise interference on sentence vector representation,thereby fostering the aggregation of sen-tences sharing the same aspect category within the embedding space.Experimental results validate the effectiveness of our proposed ProtPrompt model on three publicly available datasets.The experimental results show that the model improves the F1 score over the state-of-the-art(SOTA)model by 4.35%,8.62%,and 8.39%for the three publicly available datasets,respectively.This substantiates its ability to efficiently detect aspect categories.关键词
方面类别检测/原型网络/提示学习/多标签分类/元学习Key words
aspect category detection/prototypical network/prompt learning/multi-label classification/meta-learning分类
数理科学引用本文复制引用
管超峰,白宇,周贤雷..基于提示增强原型网络的小样本多标签方面类别检测[J].山西大学学报（自然科学版）,2024,47(3):494-505,12.基金项目
国家自然科学基金(U1908216) （U1908216）
