MV-SHIF:多视图对称假设推断融合网络用于在文档中提取情感-原因对
Cheng Yang1, Hua Zhang1, Bi Chen1
1School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, Zhejiang, PR China 310018.
概括
这项研究引入了一种新的方法,用于使用文本包含的情感原因对提取 (ECPE). 该方法通过过不相关的句子和捕捉关系语义来提高准确性,实现最先进的结果.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 情感原因对提取 (ECPE) 具有挑战性,因为难以区分有效对和不相关的对.
- 现有的方法通常依赖于多任务学习来进行语义提取,但忽视了明确的句子关系编码.
研究的目的:
- 通过纳入文本包容性范式,为ECPE提出一种新的方法.
- 通过过不相关的条款和有效地捕捉情感因果关系语义来提高ECPE的性能.
主要方法:
- 制定ECPE作为文本包容任务,推断文档 (前提) 和条款/对 (假设) 之间的关系.
- 设计标签视图假设模板来过不相关的句子,以及关系语义的多视图对称模板.
- 使用简化囊网络进行信息融合和文本包含识别.
主要成果:
- 拟议的模型实现了ECPE任务的最新性能.
- 与之前的基线方法相比,显示出显著的改善.
结论:
- 文本包容性范式为ECPE提供了一种新且有效的方法.
- 开发的多视图假设推断和信息融合技术增强了从文本的因果发现.
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