1. Using Link Information to Enhance Web Page Classification
Xiaoguang Qi, Brian D. Davison

2. Introduction Web page classification is important
Browsing information through topics
Query result tagging
Finding similar documents
Clustering query results
Applying textual classifiers on web data
Not satisfying

3. Our Approach
Using information of neighboring pages to help judge a page’s topic
Four kinds of neighbors
Parents, children, siblings, co-spouses
Neighboring pages may have been labeled
Appearing in web hierarchies
Use the labels is available
Pages without existing labels: use a classifier

4. Other Considerations
Are the four kinds of neighbors equally important?
Give them different weights
β = (β1, β2, β3, β4)
The use of classifier may introduce noise
Down-weight the results of classifier: η
0≤η≤1

5. Other Considerations (Cont.)
Do intra-host links count?
They are often down-weighted or ignored in link-based ranking
Web page classification is a different scenario
Give it a weight: θ (θ =0,1)
Counting the multiple paths
Siblings may have multiple parents in common
Weighted path version vs. unweighted path version

6. Other Considerations (Cont.)
Combining neighbors with the start page
Weighted average: α (0≤α≤1 )
α* start page+(1- α)*neighbors

7. Experimental Setup 12 top-level categories in DMoz Directory
19,000 document from each category to train the text classifier
1,000 for testing
Get incoming links by querying Yahoo API

8. Parameter Tuning

9. Parameter Tuning (Cont.)

10. Parameter Tuning (Cont.)

11. Parameter Tuning (Cont.)

12. Experimental Results Best performance is achieved at the settings:
α=0.2, β= (0, 0, 1, 0), η=0, θ=1, weighted path version

13. Experimental Results (Cont.)
“DMoz copy effect”
We are benefiting from it!
It may affect the optimal parameter setting
Solution
Remove the pages whose URL contains directory names of DMoz
E.g. “Computers/Hardware”, “Business/Employment”

14. Conclusion Improved the accuracy of web page classification
Explored the effects of a number of parameters

15. Future Work Is the parameter tuning independent of dataset?
Is our dataset representative of the web?
Other classifiers
What’s the effect of the number of categories and the granularity of the categories