图 书 馆
用主题关系模型学习文本和结构之间的依赖关系TRM - Learning Dependencies between Text and Structure with Topical Relational Models |
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| 课程网址: | http://ocean.nit.net.cn/subject/user/index.php?do=addmd&typeid=37 |
| 主讲教师: | Rudi Studer |
| 开课单位: | 卡尔斯鲁厄理工学院 |
| 开课时间: | 2013-11-28 |
| 课程语种: | 英语 |
| 中文简介: |  通过对实体（例如人，位置或组织）之间的异构结构信息以及相关的文本信息进行编码，富文本的结构化数据在Web和企业数据库上变得越来越普遍。为了分析这种类型的数据，针对文档集合高度定制的现有主题建模方法需要手动定义的正则化术语才能利用主题学习并将其偏向于结构信息。我们提出一种称为主题关系模型的方法，作为一种从文本和结构信息中自动学习主题的原则方法。使用主题模型，我们可以证明我们的方法在利用异构结构信息方面是有效的，胜过了需要手动调整正则化的最新方法。 |
| 课程简介: | Text-rich structured data become more and more ubiquitous on the Web and on the enterprise databases by encoding heterogeneous structural information between entities such as people, locations, or organizations and the associated textual information. For analyzing this type of data, existing topic modeling approaches, which are highly tailored toward document collections, require manually-defined regularization terms to exploit and to bias the topic learning towards structure information. We propose an approach, called Topical Relational Model, as a principled approach for automatically learning topics from both textual and structure information. Using a topic model, we can show that our approach is effective in exploiting heterogeneous structure information, outperforming a state-of-the-art approach that requires manually-tuned regularization. |
| 关 键 词: | 数据库; 模型学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-30：zyk |
| 最后编审: | 2020-11-30：zyk |
| 阅读次数: | 30 |