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从前所未有的文本中学习概念图
Learning concept graphs from text with stick-breaking priors |
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| 课程网址: |
http://videolectures.net/nips2010_chambers_lcg/
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| 主讲教师: |
America Chambers
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| 开课单位: |
加利福尼亚大学
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| 开课时间: |
2011-03-25
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| 课程语种: |
英语
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| 中文简介: |
我们提出了一种用于学习一般图形结构的生成概率模型，我们用文本来概括概念图。概念图提供了文档集合的主题内容的可视摘要，这是仅使用关键字搜索难以实现的任务。所提出的模型可以学习不同类型的概念图结构，并且能够利用关于图结构的部分先验知识以及标记文档。我们描述了一个基于图形断纸过程的生成模型，以及马尔可夫链蒙特卡罗推理过程。对模拟数据的实验表明，该模型可以在无监督和半监督模式下进行学习时恢复已知的图形结构。我们还表明，所提出的模型在经验对数似然方面与现实世界文本数据集上基于现有结构的主题模型（例如hPAM和hLDA）具有竞争力。最后，我们说明了模型在更新维基百科类别图的问题中的应用。
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| 课程简介: |
We present a generative probabilistic model for learning general graph structures, which we term concept graphs, from text. Concept graphs provide a visual summary of the thematic content of a collection of documents-a task that is difficult to accomplish using only keyword search. The proposed model can learn different types of concept graph structures and is capable of utilizing partial prior knowledge about graph structure as well as labeled documents. We describe a generative model that is based on a stick-breaking process for graphs, and a Markov Chain Monte Carlo inference procedure. Experiments on simulated data show that the model can recover known graph structure when learning in both unsupervised and semi-supervised modes. We also show that the proposed model is competitive in terms of empirical log likelihood with existing structure-based topic models (such as hPAM and hLDA) on real-world text data sets. Finally, we illustrate the application of the model to the problem of updating Wikipedia category graphs.
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| 关 键 词: |
图形结构; 概念图; 可视摘要
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-07-25：cwx
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| 阅读次数: |
59
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