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无监督预测引文影响

Unsupervised Prediction of Citation Influences
课程网址: http://videolectures.net/icml07_dietz_upc/  
主讲教师: Laura Dietz
开课单位: 马克斯普朗克研究所
开课时间: 2007-06-23
课程语种: 英语
中文简介:
出版物库包含有关科学研究领域发展的大量信息。我们解决了创建研究领域可视化的问题,该研究领域描述了论文之间的主题流,量化了论文对彼此的影响,并有助于确定关键贡献。为此,我们设计了一个解释文档生成的概率主题模型;该模型通过引用融合了主题创新和主题继承的各个方面。我们评估模型预测引用对手动评级引用的影响强度的能力。
课程简介: Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.
关 键 词: 出版物库; 论文; 解释文档
课程来源: 视频讲座网
最后编审: 2019-04-17:lxf
阅读次数: 48