高斯分层潜在狄利克雷分配：让一词多义回归
Gaussian Hierarchical Latent Dirichlet Allocation: Bringing Polysemy Back
AI总结：
针对高斯潜在狄利克雷分配无法捕捉一词多义的问题，本文提出高斯分层LDA，通过主题分层结构恢复多义建模能力，并提升主题一致性、预测准确率与表示简约性。
摘要：
主题模型被广泛用于发现一组文档的潜在表示。两个经典模型是潜在狄利克雷分配和高斯潜在狄利克雷分配，其中前者使用词上的多项分布，后者则使用预训练词嵌入向量上的多元高斯分布作为潜在主题表示。与潜在狄利克雷分配相比，高斯潜在狄利克雷分配的局限在于它无法捕捉一个词的一词多义，例如“bank”。在本文中，我们表明，通过在模型可用于表示给定文档的主题集合中引入分层结构，高斯潜在狄利克雷分配可以恢复捕捉一词多义的能力。我们的高斯分层潜在狄利克雷分配与基于高斯的模型相比显著改进了一词多义检测，并且与分层潜在狄利克雷分配相比提供了更简约的主题表示。我们大量的定量实验表明，在广泛的语料库和词嵌入向量上，我们的模型还取得了更好的主题一致性和留出文档预测准确率。
英文摘要：
Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as ``bank.'' In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
