On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
S-CREAM - Semi-automatic CREAtion of Metadata
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
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
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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
Collective information extraction with relational Markov networks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
iASA: learning to annotate the semantic web
Journal on Data Semantics IV
Academic conference homepage understanding using constrained hierarchical conditional random fields
Proceedings of the 17th ACM conference on Information and knowledge management
Extracting the author of web pages
Proceedings of the 2nd ACM workshop on Information credibility on the web
Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Towards a SVM-struct Based Active Learning Algorithm for Least Cost Semantic Annotation
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Identifying Information Sender Configuration of Web Pages
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
PORE: positive-only relation extraction from wikipedia text
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
A Combination Approach to Web User Profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Chinese frame identification using T-CRF model
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Topic-level social network search
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tree representations in probabilistic models for extended named entities detection
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Generating extractive summaries of scientific paradigms
Journal of Artificial Intelligence Research
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The large volume of web content needs to be annotated by ontologies (called Semantic Annotation), and our empirical study shows that strong dependencies exist across different types of information (it means that identification of one kind of information can be used for identifying the other kind of information). Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling the dependencies to do better annotation. However, as information on a Web page is not necessarily linearly laid-out, the previous linear-chain CRFs have their limitations in semantic annotation. This paper is concerned with semantic annotation on hierarchically dependent data (hierarch-ical semantic annotation). We propose a Tree-structured Conditional Random Field (TCRF) model to better incorporate dependencies across the hierarchic-ally laid-out information. Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.