图 书 馆
多项主题模型的自动标注Automatic Labeling of Multinomial Topic Models |
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| 课程网址: | http://videolectures.net/kdd07_mei_alm/ |
| 主讲教师: | Qiaozhu Mei |
| 开课单位: | 伊利诺伊大学 |
| 开课时间: | 2007-08-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multinomial distributions over words are frequently used to model topics in text collections. A common, major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic. So far, such labels have been generated manually in a subjective way. In this paper, we propose probabilistic approaches to automatically labeling multinomial topic models in an objective way. We cast this labeling problem as an optimization problem involving minimizing Kullback-Leibler divergence between word distributions and maximizing mutual information between a label and a topic model. Experiments with user study have been done on two text data sets with different genres. The results show that the proposed labeling methods are quite effective to generate labels that are meaningful and useful for interpreting the discovered topic models. Our methods are general and can be applied to labeling topics learned through all kinds of topic models such as PLSA, LDA, and their variations. |
| 关 键 词: | 模型自动标注; PLSA; LDA及其变体 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-06-21：chenxin |
| 阅读次数: | 91 |