On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Machine Learning
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Information Retrieval
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Browsing Schedules - An Agent-Based Approach to Navigating the Semantic Web
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Evaluating machine learning for information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Integer linear programming inference for conditional random fields
ICML '05 Proceedings of the 22nd international conference on Machine learning
2D Conditional Random Fields for Web information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Simultaneous record detection and attribute labeling in web data extraction
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive information extraction with constrained conditional random fields
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Using uneven margins SVM and perceptron for information extraction
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Tree-structured conditional random fields for semantic annotation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
OntoWiki – a tool for social, semantic collaboration
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
IRSG'98 Proceedings of the 20th Annual BCS-IRSG conference on Information Retrieval Research
A social recommendation framework based on multi-scale continuous conditional random fields
Proceedings of the 18th ACM conference on Information and knowledge management
FireCite: lightweight real-time reference string extraction from webpages
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Mining topic-level opinion influence in microblog
Proceedings of the 21st ACM international conference on Information and knowledge management
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We address the problem of academic conference homepage understanding for the Semantic Web. This problem consists of three labeling tasks - labeling conference function pages, function blocks, and attributes. Different from traditional information extraction tasks, the data in academic conference homepages has complex structural dependencies across multiple Web pages. In addition, there are logical constraints in the data. In this paper, we propose a unified approach, Constrained Hierarchical Conditional Random Fields, to accomplish the three labeling tasks simultaneously. In this approach, complex structural dependencies can be well described. Also, the constrained Viterbi algorithm in the inference process can avoid logical errors. Experimental results on real world conference data have demonstrated that this approach performs better than cascaded labeling methods by 3.6% in F1-measure and that the constrained inference process can improve the accuracy by 14.3%. Based on the proposed approach, we develop a prototype system of use-oriented semantic academic conference calendar. The user simply needs to specify what conferences he/she is interested in. Subsequently, the system finds, extracts, and updates the semantic information from the Web, and then builds a calendar automatically for the user. The semantic conference data can be used in other applications, such as finding sponsors and finding experts. The proposed approach can be used in other information extraction tasks as well.