论文标题
贝叶斯等级单词表示学习
Bayesian Hierarchical Words Representation Learning
论文作者
论文摘要
本文介绍了学习算法的贝叶斯分层词表示（BHWR）。 BHWR促进了差异贝叶斯单词表示学习与语义分类法建模通过分层先验结合在一起。通过在相关单词之间传播相关信息，BHWR利用分类法来提高此类表示的质量。对几个语言数据集的评估证明了BHWR比合适的替代方案的优势，该替代方案有助于使用或没有语义的贝叶斯建模。最后，我们进一步表明，BHWR为稀有词提供了更好的表示。
This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.
