图 书 馆
基于Dirichlet多项式回归的任意特征的主题模型Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression |
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| 课程网址: | http://videolectures.net/uai08_mccallum_tmcaf/ |
| 主讲教师: | Andrew McCallum |
| 开课单位: | 马萨诸塞大学 |
| 开课时间: | 2008-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document metadata. In this paper we propose a Dirichlet-multinomial regression (DMR) topic model that includes a log-linear prior on document-topic distributions that is a function of observed features of the document, such as author, publication venue, references, and dates. We show that by selecting appropriate features, DMR topic models can meet or exceed the performance of several previously published topic models designed for specific data. |
| 关 键 词: | 建模内容文本文件; 文档元数据; 多项式回归主题模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 236 |