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.

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

31. Our Model (3) Dirichlet Process Prior ( Infinite Mixture Model ) N Text Fragment S Different Web Site

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.

35. Generation Process (1)

42. Variational Method (6) Mixture of tokens Binary A set of binary features Conjugate priors

50. Results of Attribute Resolution

54. Questions and Answers

58. Variational Inference (4) Mixture of tokens Binary A set of binary features Conjugate priors