MPGM:多提示生成模型与自我监督的对比学习,以获得方面情绪三重提取
Kun Yang1, Liansong Zong1, Mingwei Tang1
1School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China.
概括
这项研究引入了一种新的多提示生成模型 (MPGM) 来改进复杂句子中的 Aspect Sentiment Triplet Extraction (ASTE). MPGM增强了多跨度术语和完整的三重词的识别,优于现有的方法.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 生成模型在方面情感三重提取 (ASTE) 中表现有前途.
- 现有的方法难以处理含有多跨度术语的复杂句子,导致不完全的三重识别.
- 需要改进的生成模型来处理ASTE中复杂的句子结构.
研究的目的:
- 提出一种新的多提示生成模型 (MPGM),用于增强的方面情感三重提取 (ASTE).
- 为了解决识别长度术语的局限性,并在复杂的句子中完成三重词.
- 为了提高识别方面术语,情感极性和意见术语关系的准确性.
主要方法:
- 开发了一个多提示生成模型 (MPGM),集成多个提示模板以减轻单个模板错误.
- 设计了一个术语亲和度评估,以提高术语之间的关系的识别.
- 实施了双维监督对比学习策略,以改善三倍跨度表示.
主要成果:
- 与现有方法相比,MPGM在两个公共数据集 (ASTE-DATA-v1和ASTE-DATA-v2) 上显示出更高的性能.
- 在多个子集中获得了更好的F1分数,包括14Lap,14Res,15Res和16Res.
- 该模型有效地增强了术语和情感极性之间的关系的建模,即使在复杂的句子.
结论:
- 拟议的MPGM有效地解决了ASTE当前生成模型的局限性.
- 该模型在处理复杂句子和多跨度术语方面取得了显著的进步,以准确地提取三重组.
- MPGM提供了一种更全面,更精确的方法来提取视角情感三位数.
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