图 书 馆
主题模型中突发事件的解释Accounting for Burstiness in Topic Models |
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| 课程网址: | http://videolectures.net/icml09_doyle_abtm/ |
| 主讲教师: | Gabriel Doyle |
| 开课单位: | 加州大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many different topic models have been used successfully for a variety of applications. However, even state-of-the-art topic models suffer from the important flaw that they do not capture the tendency of words to appear in bursts; it is a fundamental property of language that if a word is used once in a document, it is more likely to be used again. We introduce a topic model that uses Dirichlet compound multinomial (DCM) distributions to model this burstiness phenomenon. On both text and non-text datasets, the new model achieves better held-out likelihood than standard latent Dirichlet allocation (LDA). It is straightforward to incorporate the DCM extension into topic models that are more complex than LDA. |
| 关 键 词: | 主题模型; 狄利克雷分配; 计算机科学; 文本挖掘 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-07：chenjy |
| 最后编审: | 2023-03-07：chenjy |
| 阅读次数: | 15 |