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摘要翻译：
统计主题模型为从大量文本文档中自动发现高级知识提供了一个通用的数据驱动框架。虽然主题模型可以潜在地在数据集中发现广泛的主题，但所学主题的可解释性并不总是理想的。另一方面，人类定义的概念由于仔细选择词来定义概念，往往语义更丰富，但它们往往不能详尽地涵盖数据集中的主题。在本文中，我们提出了一个概率框架，将人类定义的语义概念的层次结构与统计主题模型相结合，以寻求两个世界的最佳选择。使用两个不同的概念层次结构和两个文本文档集合的实验结果表明，这种组合导致了相关语言模型质量的系统改进，以及用于推断和可视化文档语义的新技术。
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英文标题：
《Text Modeling using Unsupervised Topic Models and Concept Hierarchies》
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作者：
Chaitanya Chemudugunta, Padhraic Smyth and Mark Steyvers
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Information Retrieval 信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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英文摘要：
Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. While topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, on the other hand, tend to be semantically richer due to careful selection of words to define concepts but they tend not to cover the themes in a data set exhaustively. In this paper, we propose a probabilistic framework to combine a hierarchy of human-defined semantic concepts with statistical topic models to seek the best of both worlds. Experimental results using two different sources of concept hierarchies and two collections of text documents indicate that this combination leads to systematic improvements in the quality of the associated language models as well as enabling new techniques for inferring and visualizing the semantics of a document.
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PDF链接：
https://arxiv.org/pdf/0808.0973
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