论文标题
高斯分层潜在迪里奇莱特分配：带回多义
Gaussian Hierarchical Latent Dirichlet Allocation: Bringing Polysemy Back
论文作者
论文摘要
主题模型被广泛用于发现一组文档的潜在表示。这两个规范模型是潜在的dirichlet分配，高斯潜在的dirichlet分配，前者在单词上使用多项式分布，后者在预训练的单词嵌入向量上使用多变量高斯分布作为潜在主题表示。与潜在的Dirichlet分配相比，高斯潜在的dirichlet分配是有限的，因为它不能捕获单词的多义，例如``银行''。与基于高斯的模型相比，我们的高斯层次层次分配分配可显着改善多义检测，并提供与层次潜在的dirichlet分配相比提供更简约的主题表示。我们广泛的定量实验表明，我们的模型还可以在广泛的语料库和单词嵌入向量上实现更好的主题连贯性和固定文档的预测准确性。
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.
