图 书 馆
阅读茶叶：人类如何解读主题模型Reading Tea Leaves: How Humans Interpret Topic Models |
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| 课程网址: | http://videolectures.net/nips09_boyd_graber_rtl/ |
| 主讲教师: | Jordan Boyd-Graber |
| 开课单位: | 马里兰大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Probabilistic topic models are a commonly used tool for analyzing text data, where the latent topic representation is used to perform qualitative evaluation of models and guide corpus exploration. Practitioners typically assume that the latent space is semantically meaningful, but this important property has lacked a quantitative evaluation. In this paper, we present new quantitative methods for measuring semantic meaning in inferred topics. We back these measures with large-scale user studies, showing that they capture aspects of the model that are undetected by measures of model quality based on held-out likelihood. Surprisingly, topic models which perform better on held-out likelihood may actually infer less semantically meaningful topics. |
| 关 键 词: | 概率; 文本数据; 语义 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-06：lxf |
| 阅读次数: | 54 |