A Probabilistic Framework from Information Extraction Models

摘要：

Information extraction (IE) is the problem of constructing a knowledge base from a corpus of text documents. In recent years, uncertain data applications have grown in importance in the large number of real-world applications, and IE as an uncertain data source. This paper investigated the uncertain data represent and presented a probabilistic framework from IE model that adapting principles of a state-of-the-art statistical model-semi-Conditional Random Fields (semi-CRFs), which provides a sound probability distribution over extractions.

关键词： inforniation extraction probabitistic dato conditional randomfields

作者: Ming He Yong-ping Du Shi-rui Yan

作者单位: College of Computer Science Beijing University of Technology Beijing, China

会议类型: 国际会议

会议名称: The 2nd International Conference on Software Engineering and Data Mining(IEEE 第二届国际软件工程和数据挖掘学术大会 SEDM 2010)

会议地点: 成都

会议语种:英文

页码: 325-327

在线出版日期: 2010-06-23（万方平台首次上网日期，不代表论文的发表时间）