Semantic entity detection by integrating CRF and SVM

Semantic entity detection is very important for extracting and representing the abundant semantic information of multimedia documents. 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 machines), and present its application to semantic entity detection. Using this framework, context features are represented as the probability of entity 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 may outperform traditional algorithms that only adopt part of the features.