1. Link Distribution in Wikipedia
Kwanghee Park 03/11

2. Introduction
Multilingual Document has their own background knowledge and characteristics
When Synchronize each other, we have to analysis each characteristics and supply each other

3. Introduction Focus on Wikipedia Interlanguage links and internal links
Find topic distribution of each article which are connected by Interlanguage links and analysis them.

4. Process overview L L’ Term Set Term Set Doc 1 Doc2 Doc 3
Topic n
Topic n
Term
Set
Term
Set
Topic
Topic
Topic 3
Topic 3
Topic 2
Topic 2
Topic 1
Topic 1
Doc Doc Doc 3
Doc 1’ Doc2 ‘ Doc 3’

5. Process : LDA modeling Decide number of topic in each language
Because they have deferent amount of terms
English>>Espanola≒French >Chances>>Korean
Topic assigning
Have to translation terms into pivot language
Translation by voting
Wikipedia Interlanguage link
Google translation
Any other dictionary

6. Experiment Target domain Tools Disease : 208 number of Doc
Settlement : 1328 number of Doc
Tools
Lingpipe LDA api

7. Experiment - topic number
Focus on section heading number
Total number of section heading :1215
Used over 3 times : 121
Used over 10 times : 36
Clustering 13times
10,20,30,40,50,75,100,125,150,175, 200,225,250

8. Experiment - topic number 30

9. Experiment - topic number 50

10. Experiment - topic number 100

11. Experiment - topic number 200

12. Experiment World_Health_Organization Census_2000
Andalucía,_Valle_del_Cauca
inflammation
Hashimoto's_thyroiditis
Fever
Hadley_cell
Obesity
cancer
Disability-adjusted_life_year
Max_Rubner
Infection
Surgery
Centers_for_Disease_Control_and_Prevention
headache
Insomnia
diarrhea
Census_in_the_United_Kingdom
Immune_system
gene
Adhesin

13. Experiment Peak topic appearance
Experiment specific domain
Able to predict shape of topic distribution based on this peek topic
Peak
topic
Peak
topic
Peak
topic

14. Decide topic number Ignore peak topic
Choose well topic distribution ignoring peak topic
Well distribution means topics are spread all over the document
English = 100
Korean = 50
French, chances, Espanola = 75

15. Future work Term expansion Topic assigning
Link  terms in doc without stop ward
Topic assigning
Translation issue
Translation by voting
Wikipedia Interlanguage link
Google translation
Any other dictionary
Not decided detail methods yet
Medline
Snomed

16. Template recommend
Cluster several specific domain documents with unclassified text
Classifying domain by analyzing topic distribution of text

17. Disease + Settlement T=50

18. Starvation Trenton,_New_Jersey
Starvation  disease
Trenton,_New_Jersey  Settlement

19. Disease + Settlement T=75

20. Disease + Settlement T = 100

21. Thanks