图 书 馆
钻研：维基百科文本中的正相关提取PORE: Positive-Only Relation Extraction from Wikipedia Text |
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| 课程网址: | http://videolectures.net/iswc07_wang_por/ |
| 主讲教师: | Gang Wang |
| 开课单位: | 上海交通大学 |
| 开课时间: | 2008-01-31 |
| 课程语种: | 汉简 |
| 中文简介: |  |
| 课程简介: | Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly outperforms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains. |
| 关 键 词: | 信息冗余; 维基百科; 提取语义 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-30：lxf |
| 阅读次数: | 73 |