图 书 馆
多语种文本分析的对称对应主题模型Symmetric Correspondence Topic Models for Multilingual Text Analysis |
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| 课程网址: | http://videolectures.net/machine_fukumasu_models/ |
| 主讲教师: | Kosuke Fukumasu |
| 开课单位: | 神户大学 |
| 开课时间: | 2013-04-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Topic modeling is a widely used approach to analyzing large text collections. A small number of multilingual topic models have recently been explored to discover latent topics among parallel or comparable documents, such as in Wikipedia. Other topic models that were originally proposed for structured data are also applicable to multilingual documents. Correspondence Latent Dirichlet Allocation (CorrLDA) is one such model; however, it requires a pivot language to be specified in advance. We propose a new topic model, Symmetric Correspondence LDA (SymCorrLDA), that incorporates a hidden variable to control a pivot language, in an extension of CorrLDA. We experimented with two multilingual comparable datasets extracted from Wikipedia and demonstrate that SymCorrLDA is more effective than some other existing multilingual topic models. |
| 关 键 词: | 主题建模; 文本集合; 数据集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-05-20：wangjt |
| 阅读次数: | 78 |