ETM: Entity Topic Models for Mining Documents Associated with Entities
ETM: Entity Topic Models for Mining Documents Associated with Entities
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DOI：
10.1109/icdm.2012.107
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Hyungsul Kim;Yizhou Sun;J. Hockenmaier;Jiawei Han
中科院分区：
文献类型：
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作者：
Hyungsul Kim;Yizhou Sun;J. Hockenmaier;Jiawei Han
主题模型将每个文档分解为不同的主题，并将每个主题表示为术语的分布，已被广泛且成功地用于更好地理解文本文档集合。然而，文档还与进一步的信息相关联，例如其中提到的真实世界实体集。例如，新闻文章通常与几个人，组织，国家或地点有关。由于这些关联的实体携带了丰富的信息，因此非常希望构建更具表达力的基于实体的主题模型，该模型可以捕获每个主题、每个实体以及每个主题-实体对的术语分布。因此，在本文中，我们介绍了一种新的实体主题模型（ETM）的文档，与一组实体。ETM不仅可以模拟给定主题和实体信息的术语生成过程，还可以模拟实体术语分布与主题术语分布之间的相关性。提出了一种基于Gibbs采样的学习算法。在真实的数据集上的实验证明了我们的方法在几个最先进的基线上的有效性。
Topic models, which factor each document into different topics and represent each topic as a distribution of terms, have been widely and successfully used to better understand collections of text documents. However, documents are also associated with further information, such as the set of real-world entities mentioned in them. For example, news articles are usually related to several people, organizations, countries or locations. Since those associated entities carry rich information, it is highly desirable to build more expressive, entity-based topic models, which can capture the term distributions for each topic, each entity, as well as each topic-entity pair. In this paper, we therefore introduce a novel Entity Topic Model (ETM) for documents that are associated with a set of entities. ETM not only models the generative process of a term given its topic and entity information, but also models the correlation of entity term distributions and topic term distributions. A Gibbs sampling-based algorithm is proposed to learn the model. Experiments on real datasets demonstrate the effectiveness of our approach over several state-of-the-art baselines.