基于Copula的短文本神经主题建模

Lihui Lin, Hongyu Jiang, Yanghui Rao
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引用次数: 14

摘要

从文档中提取主题信息对于舆情分析、文本分类和信息检索任务具有重要意义。与从长文档中识别各种主题相比,为每条短消息生成集中的主题分布具有挑战性。虽然可以通过调整潜狄利克雷分配等传统主题模型的超参数来解决这一问题,但它仍然是神经主题建模中的一个开放性问题。在本文中,我们将流行的基于自编码变分贝叶斯的神经主题模型应用于短文本,通过探索阿基米德copulas来指导由重新参数化后验分布的线性投影样本导出的估计主题分布。实验结果表明,与现有神经主题模型相比,该方法在困惑度、主题一致性和分类精度方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Copula Guided Neural Topic Modelling for Short Texts
Extracting the topical information from documents is important for public opinion analysis, text classification, and information retrieval tasks. Compared with identifying a wide variety of topics from long documents, it is challenging to generate a concentrated topic distribution for each short message. Although this problem can be tackled by adjusting the hyper-parameters in traditional topic models such as Latent Dirichlet Allocation, it remains an open problem in neural topic modelling. In this paper, we focus on adapting the popular Auto-Encoding Variational Bayes based neural topic models to short texts, by exploring the Archimedean copulas to guide the estimated topic distributions derived from linear projected samples of re-parameterized posterior distributions. Experimental results show the superiority of our method when compared with existing neural topic models in terms of perplexity, topic coherence, and classification accuracy.
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