The document discusses techniques for web information extraction (WIE). It describes an early approach (WIE 1.0) that used contextual patterns to extract information about specific categories like cheeses. It then presents an improved approach (WIE 1.1) that uses a dual iterative process to extract both instances and patterns. Finally, it discusses comparing different methods for assessing the accuracy of extracted patterns and instances, and outlines a vision for WIE 2.0 that performs continuous learning to discover new categories and relations.

1. Pattern Learning for Web Information Extraction Justin Betteridge IR Series Seminar May 23, 2008 Tom Mitchell, Andrew Carlson Sue Ann Hong, Edith Law, Sophie Wang, Estevam Hruschka

2. Outline Web Information Extraction (WIE) 1.0 Contextual Patterns Definition Assessment Usage Comparing Pattern Assessment Methods WIE 2.0

3. WIE 1.0 Cheese Cheddar Swiss Gouda The Web

4. WIE 1.0 Cheese Cheddar Swiss Gouda __ such as __ such __ as __ … cheeses, including __ … The Web

5. WIE 1.0 Cheese Google Cheddar Swiss Gouda __ such as __ such __ as __ … cheeses, including __ … Kraft manufactures a variety of cheeses, including Feta cheese . Companies who market a variety of cheeses, including Kraft Foods , … The Web

6. WIE 1.0 Cheese Google Cheddar Swiss Gouda __ such as __ such __ as __ … cheeses, including __ … Kraft manufactures a variety of cheeses, including Feta cheese . Companies who market a variety of cheeses, including Kraft Foods , … The Web

7. WIE 1.0 Cheese Google Cheddar Swiss Gouda __ such as __ such __ as __ … cheeses, including __ … Kraft manufactures a variety of cheeses, including Feta cheese . Companies who market a variety of cheeses, including Kraft Foods , … The Web

8. WIE 1.0 Cheese Google Cheddar Swiss Gouda __ such as __ such __ as __ … cheeses, including __ … Kraft manufactures a variety of cheeses, including Feta cheese . Companies who market a variety of cheeses, including Kraft Foods , … Feta Kraft Foods The Web

9. WIE 1.1 Cheese Google The Web Gouda cheeses, including __ Swiss cheeses such as __ Cheddar

10. WIE 1.1 Cheese Google A slice of cheddar cheese goes well with … The Web Gouda cheeses, including __ Swiss cheeses such as __ Cheddar

11. WIE 1.1 Cheese Google A slice of cheddar cheese goes well with … The Web Gouda cheeses, including __ Swiss cheeses such as __ Cheddar

12. WIE 1.1 Cheese Google A slice of cheddar cheese goes well with … a slice of __ cheese __ goes well with The Web Gouda cheeses, including __ Swiss cheeses such as __ Cheddar

13. Dual Iterative Approach Given seeds (instances and/or patterns) Using instances, extract & assess candidate patterns Using patterns, extract & assess candidate instances Repeat

14. WIE 1.1 Cheese Google A slice of cheddar cheese goes well with … a slice of __ cheese __ goes well with The Web Gouda cheeses, including __ Swiss cheeses such as __ Cheddar

15. Assessing Instances PMI :cheeses such as __ PMI :a slice of __ … Cheese? NB classifier

16. Point-wise Mutual Information score( choice ) = log2(p( problem & choice ) / (p( problem )p( choice ))) Turney, 2001

17. Patterns: FAQ What does one look like? How can I tell which ones are good? What can I use them for?

18. Patterns Defined mayo and a lot of cheddar cheese can make a sandwich yummy

19. Patterns Defined mayo and a lot of cheddar cheese can make a sandwich yummy ... cheddar cheese soaked in brine dairy products like cheddar cheese … NP Prep VP Prep NP

20. Patterns Assessed Cheese a slice of __ cheese __ goes well with Gouda cheeses, including __ Swiss cheeses such as __ Cheddar

21. Patterns Assessed Cheese a slice of __ cheese __ goes well with H1: Must co-occur with at least 2 seed instances H2: Sort by estimated precision H1: Must co-occur with at least 2 seed instances H2: Must not co-occur with any negative seed instances H3: Must contain a trigger word Gouda cheeses, including __ Swiss cheeses such as __ Cheddar

22. Patterns Assessed Cheese a slice of __ cheese __ goes well with H1: Must co-occur with at least 2 seed instances H2: Sort by estimated precision H1: Must co-occur with at least 2 seed instances H2: Must not co-occur with any negative seed instances H3: Must contain a trigger word Etzioni et al, 2004 Talukdar et al, 2006 Gouda cheeses, including __ Swiss cheeses such as __ Cheddar

23. Patterns Assessed PMI :cheddar PMI :swiss … Cheese? NB classifier

24. Comparing Assessment Methods Precision = portion of co-occuring NPs that are instances of the category Recall = portion of instances that co-occur Precision is more imporant (F 0.5 )

25. WIE 2.0 Category Discovery Relation Discovery from patterns!! Continuous Learning