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Record-boundary discovery in Web documents
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
An introduction to variational methods for graphical models
Learning in graphical models
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Wrapper induction: efficiency and expressiveness
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IEPAD: information extraction based on pattern discovery
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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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RoadRunner: Towards Automatic Data Extraction from Large Web Sites
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ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
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Fully automatic wrapper generation for search engines
WWW '05 Proceedings of the 14th international conference on World Wide Web
Web data extraction based on partial tree alignment
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Dynamic Trees for Unsupervised Segmentation and Matching of Image Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
2D Conditional Random Fields for Web information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Simultaneous record detection and attribute labeling in web data extraction
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Corrective feedback and persistent learning for information extraction
Artificial Intelligence
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Dynamic hierarchical Markov random fields and their application to web data extraction
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Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A new class of upper bounds on the log partition function
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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An overlapping tree approach to multiscale stochastic modeling and estimation
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Primal sparse Max-margin Markov networks
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2D correlative-chain conditional random fields for semantic annotation of web objects
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Existing template-independent web data extraction approaches adopt highly ineffective decoupled strategies---attempting to do data record detection and attribute labeling in two separate phases. In this paper, we propose an integrated web data extraction paradigm with hierarchical models. The proposed model is called Dynamic Hierarchical Markov Random Fields (DHMRFs). DHMRFs take structural uncertainty into consideration and define a joint distribution of both model structure and class labels. The joint distribution is an exponential family distribution. As a conditional model, DHMRFs relax the independence assumption as made in directed models. Since exact inference is intractable, a variational method is developed to learn the model's parameters and to find the MAP model structure and label assignments. We apply DHMRFs to a real-world web data extraction task. Experimental results show that: (1) integrated web data extraction models can achieve significant improvements on both record detection and attribute labeling compared to decoupled models; (2) in diverse web data extraction DHMRFs can potentially address the blocky artifact issue which is suffered by fixed-structured hierarchical models.