Web Information Extraction Learning based on Probabilistic Graphical Models
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The document describes a graphical model for jointly extracting and resolving product attributes from web pages. The model uses a Dirichlet process prior to handle an unlimited number of attributes. Variational inference is used to approximate the intractable posterior distribution. Experimental results on four domains show the model achieves good performance on attribute extraction and resolution without supervision.
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- 1. Web Information Extraction Learning based on Probabilistic Graphical Models Wai Lam Joint work with Tak-Lam Wong The Chinese University of Hong Kong
- 3. Wrapper Adaptation Problem (1)
- 4. Wrapper Adaptation Problem (2) Learned wrapper Wrapper learning
- 9. Motivating Example (Source: http://www.superwarehouse.com ) (Source: http://www.crayeon3.com )
- 16. Problem Definition (2) <TR> <TD> <P> <SPAN> White balance </SPAN> </P> </TD> <TD> <P> <SPAN> Auto, daylight, cloudy, tungstem, fluorescent, fluorescent H, custom </SPAN> </P> </TD> </TR> <TR> Line separator Line separator
- 17. Problem Definition (3) Attribute information Target information Layout information Content information White balance Auto, daylight, … … boldface, in-table 1 (related to attribute) white balance
- 18. Problem Definition (4) Attribute information Target information Layout information Content information View larger image boldface, underline 0 (irrelevant) not-an-attribute
- 27. Graphical Models (8) θ i N x i G Finite Mixture Model
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- 32. Our Model (4) N Text Fragment Target information Layout information Content information Dirichlet Process Prior ( Infinite Mixture Model ) The proportion of the k -th component in the mixture Content information parameter of the k -th component Target information parameter of the k-th component
- 33. Our Model (5) S Different Web Site Site-dependent Layout format
- 34. Our Model (6) Dirichlet Process Prior ( Infinite Mixture Model ) Concentration parameter for DP Base distribution for content info. Base distribution for target info.
- 42. Variational Method (6) Mixture of tokens Binary A set of binary features Conjugate priors
- 50. Results of Attribute Resolution
- 58. Variational Inference (4) Mixture of tokens Binary A set of binary features Conjugate priors