基于层次主题建模的无监督术语本体学习
Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
摘要：
在本文中，我们提出了层次关系为基础的潜在狄利克雷分配（hrLDA），数据驱动的层次主题模型，从大量的异构文档中提取术语本体。与传统的主题模型相比，hrLDA依赖于名词短语而不是一元语法，考虑语法和文档结构，并通过主题关系丰富主题层次结构。通过一系列的实验，我们证明了hrLDA优于现有的主题模型，特别是在建立层次结构。此外，我们说明了鲁棒性的hrLDA在噪声数据集的设置，这是可能发生在许多实际情况下。我们的本体评价结果表明，从hrLDA提取的本体是非常有竞争力的领域专家创建的本体。
英文摘要：
In this paper, we present hierarchical relationbased latent Dirichlet allocation (hrLDA), a data-driven hierarchical topic model for extracting terminological ontologies from a large number of heterogeneous documents. In contrast to traditional topic models, hrLDA relies on noun phrases instead of unigrams, considers syntax and document structures, and enriches topic hierarchies with topic relations. Through a series of experiments, we demonstrate the superiority of hrLDA over existing topic models, especially for building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the settings of noisy data sets, which are likely to occur in many practical scenarios. Our ontology evaluation results show that ontologies extracted from hrLDA are very competitive with the ontologies created by domain experts.
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。
