English(EN) KESA: A Knowledge Enhanced Approach For Sentiment Analysis

KESA通过新颖的辅助任务增强情感分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员开发了KESA，一种用于增强预训练语言模型情感分析的新颖方法。该方法利用两个辅助任务：情感词填空，根据整体极性选择正确的情感词；以及条件情感预测，从词语情感推断极性。实验表明，KESA在现有预训练模型的基础上有所改进，并且可以添加到当前知识增强的后训练模型中，代码和数据已公开。 AI

影响引入了一种更轻量级的方法，将情感知识融入语言模型，有望提高情感分析任务的性能。

排序理由该集群包含一篇详细介绍情感分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Qinghua Zhao, Shuai Ma, Shuo Ren · 2026-08-27 04:00

KESA：一种增强知识的情感分析方法

arXiv:2202.12093v2 Announce Type: replace Abstract: Though some recent works focus on injecting sentiment knowledge into pre-trained language models, they usually design mask and reconstruction tasks in the post-training phase. In this paper, we aim to benefit from sentiment know…