Sciweavers

Share
ICML
2007
IEEE

Dynamic hierarchical Markov random fields and their application to web data extraction

4 years 9 months ago
Dynamic hierarchical Markov random fields and their application to web data extraction
Hierarchical models have been extensively studied in various domains. However, existing models assume fixed model structures or incorporate structural uncertainty generatively. In this paper, we propose Dynamic Hierarchical Markov Random Fields (DHMRFs) to incorporate structural uncertainty in a discriminative manner. DHMRFs consist of two parts ? structure model and class label model. Both are defined as exponential family distributions. Conditioned on observations, DHMRFs relax the independence assumption as made in directed models. As exact inference is intractable, a variational method is developed to learn parameters and to find the MAP model structure and label assignment. We apply the model to a real-world web data extraction task, which automatically extracts product items for sale on the Web. The results show promise.
Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen
Comments (0)
books