Semantic Entity Detection by Integrating CRF and SVM
Semantic entity detection is very important for extracting and representing the abundant semantic information of multimedia doc uments. In comparison with other media, e.g. video, image and audio, text expresses semantics more directly and often serves as a bridge in cross-media analysis. However, semantic entity detection from text is still a difficult problem because of the complexity of natural language. In this paper, we propose a novel framework which takes the advantages of both CRF (conditional random fields) and SVM (support vector ma chines), and present its application to semantic entity detection. Using this framework, context features are represented as the probability of en tity boundary and extracted via CRF, and then linguistic and statistical features are extracted via large-scale text document analysis. Finally, all extracted features are integrated and used to perform the classification. As our algorithm systematically integrates the context, linguistic and statistical features, it nlay outperform traditional algorithms that only adopt part of the features.
Peng Cai Hangzai Luo Aoying Zhou
Institute of Massive Computing, East China Normal University, China
国际会议
11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)
九寨沟
英文
483-494
2010-07-14（万方平台首次上网日期，不代表论文的发表时间）