整合BERT嵌入和BiLSTM用于情感分析对话
Zhinan Gou1, Yan Li2
1School of Information Technology, Hebei University of Economics and Business, Shijiazhuang, Hebei 050061, China.
Computational intelligence and neuroscience
|June 7, 2023
概括
本研究引入了一种新的对话情绪分析方法,使用BERT和BiLSTM模型来提高人机交互的准确性. 该方法有效地捕获语义依赖关系,以便在对话性AI中更好地分类情绪.
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
- 人与计算机的交互
背景情况:
- 情绪分析对于增强人机交互的对话系统至关重要.
- 对话情感分析的挑战包括简短的文本,同义词,新词和反向的单词顺序.
- 准确的情绪分析支持客户服务和聊天机器人等应用程序.
研究的目的:
- 开发一种更准确的方法来分析对话中的情绪.
- 解决处理复杂对话特征现有方法的局限性.
- 在对话系统中改进语义理解和响应生成.
主要方法:
- 利用BERT (来自变压器的双向编码器表示) 来生成单词级和句子级向量.
- 结合BERT生成的单词级向量与BiLSTM (双向长期短期记忆) 来捕获双向语义依赖关系.
- 将单词级和句子级向量集成到线性层,用于情感分类.
主要成果:
- 拟议的BERT-BiLSTM模型与基线方法相比,表现显著改善.
- 在两个真实对话数据集上的实验结果验证了拟议方法的有效性.
- 跨不同维度的对话发言的特征建模对于情感分析准确性证明是有益的.
结论:
- 提出的方法为对话情感分析提供了一个强大的解决方案,克服了常见的挑战.
- 这一进步对提高智能对话系统性能具有重大意义.
- 整合BERT和BiLSTM提供了一个强大的框架,用于捕捉对话中的微妙情绪状态.
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