神经主题对超球的建模:使用·米塞斯-费舍尔混合物进行球形表示学习
Dayu Guo1, Zhiwen Luo2, Nizar Bouguila2
1Guangdong Provincial/Zhuhai Key Laboratory IRADS and Department of Computer Science, Beijing Normal-Hong Kong Baptist University, Zhuhai, Guangdong, 519087, China.
概括
这项研究引入了一个新的神经主题模型,使用超球几何学来更好地分析文本. ·米塞斯-菲舍尔混合神经变异主题模型 (vMNVTM) 提高了词嵌入中的主题连贯性和可解释性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 神经主题模型 (NTM) 提供可扩展的文本分析,但往往忽略嵌入几何或使用不合适的先验.
- 现有的基于变化自编码器 (VAE) 的NTM与KL分歧崩作斗争,并且缺乏针对连贯主题的定向语义.
研究的目的:
- 提出一个超球神经主题建模框架,即·米塞斯-费舍尔混合神经变化主题模型 (vMNVTM).
- 通过将·米塞斯-菲舍尔 (vMF) 分布纳入几何和定向语义来解决当前NTM的局限性.
主要方法:
- 开发了使用vMF分布的vMNVTM来模拟文档表示和主题-词关系.
- 引入了对vMF有意识的嵌入集群 (vEC) 损失和温度控制的度机制,以提高话题对齐和可分离性.
- 在潜空间中利用过球形几何学来实现方向对齐和角度分散.
主要成果:
- 在基准数据集上,vMNVTM在最先进的NTM上表现优越.
- 在主题连贯性,多样性和可解释性方面取得了改进.
- 展示了定向建模在神经主题推理中的有效性.
结论:
- 在神经主题建模中,vMF分布对于捕获定向语义至关重要.
- vMNVTM提供了一种更加连贯和可解释的方法来揭示文本中的主题结构.
- 超球模型为高级自然语言理解任务提供了显著的优势.
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