图 书 馆
文档主题挖掘:站在大数据的肩膀上Mining Topics in Documents: Standing on the Shoulders of Big Data |
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| 课程网址: | http://videolectures.net/kdd2014_chen_mining_topics/ |
| 主讲教师: | Zhiyuan (Brett) Chen |
| 开课单位: | 伊利诺伊大学 |
| 开课时间: | 2014-10-08 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (e.g., thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have so many documents. Given a small number of documents, the classic topic model LDA generates very poor topics. Even with a large volume of data, unsupervised learning of topic models can still produce unsatisfactory results. In recently years, knowledge-based topic models have been proposed, which ask human users to provide some prior domain knowledge to guide the model to produce better topics. Our research takes a radically different approach. We propose to learn as humans do, i.e., retaining the results learned in the past and using them to help future learning. When faced with a new task, we first mine some reliable (prior) knowledge from the past learning/modeling results and then use it to guide the model inference to generate more coherent topics. This approach is possible because of the big data readily available on the Web. The proposed algorithm mines two forms of knowledge: must-link (meaning that two words should be in the same topic) and cannot-link (meaning that two words should not be in the same topic). It also deals with two problems of the automatically mined knowledge, i.e., wrong knowledge and knowledge transitivity. Experimental results using review documents from 100 product domains show that the proposed approach makes dramatic improvements over state-of-the-art baselines. |
| 关 键 词: | 主题建模; 统计信息; 知识传递性 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-01：chenjy |
| 最后编审: | 2022-12-01：chenjy |
| 阅读次数: | 21 |