Collages as dynamic summaries for news video
Proceedings of the tenth ACM international conference on Multimedia
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Accessor variety criteria for Chinese word extraction
Computational Linguistics
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Web-scale named entity recognition
Proceedings of the 17th ACM conference on Information and knowledge management
Learning a two-stage SVM/CRF sequence classifier
Proceedings of the 17th ACM conference on Information and knowledge management
Personalized News Video Recommendation
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Bengali Named Entity Recognition Using Classifier Combination
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
Locating complex named entities in web text
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semantic entity-relationship model for large-scale multimedia news exploration and recommendation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
AUCWeb: A prototype for analyzing user-created web data
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Towards high-quality semantic entity detection over online forums
SocInfo'11 Proceedings of the Third international conference on Social informatics
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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.