Wiki3C : Exploiting Wikipedia for Context-aware Concept Categorization

1. Wiki3C: Exploiting Wikipedia for Context-aware Concept Categorization Presenter : Bei-YI JiangAuthors : Peng Jiang, HuimanHou, Lijiang Chen, Shimin Chen, CongleiYao, Chengkai Li, Min Wang 2013. ACM

2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

3. Motivation • Some categories are irrelevant to the text from which a concept is extracted. Most previous works treat all Wikipedia categories equallywithout considering their relative importance in different specific contexts.

4. Objectives • It leads us to a more fine-grained understanding of concepts in their contexts. The understanding can help enrich unstructured text by its relevant semantic information.

5. Methodology

6. Methodology Child Article Selection 1. Split Article Selection 2. 3.1 Basic Model 3.2 Probabilistic Model 3.3 Category Ranking Approach Category Ranking 3.

7. Methodology

8. Methodology • Child Article Selection

9. Methodology • Split Article Selection

10. Methodology • Category Ranking • Basic Model：semantic relatedness between two concept ti and tj

11. Methodology • Category Ranking • Probabilistic Model

12. Methodology • Category Ranking 0

13. Methodology • Category Ranking • relevance between a category c and a conceptt • If size of ch’(c) <K

14. Experments • manually label • randomly select 100 English articles • labeled 3072 concepts that belong to 29044 categories (7780 relevant categories)

15. Experments

16. Experments

17. Experments

18. Experments

19. Conclusions • We treat the extracted concepts surrounding a target concept as context, followed by ranking the categories of target concept according to the relevance to the context. • Experimental results prove the effectiveness of Wiki3C.

20. Comments • Advantages • effectiveness • Applications • Text clustering • Text classification