Graph-Based Word Sense Disambiguation Using Personalized PageRank

1. Personalizing PageRank for Word Sense Disambiguation Presenter: Cheng-Hui Chen Author: EnekoAgirre, AitorSoroa ECAL 2009

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

3. Motivation a written work published in printed or electronic form (出版物) I went to bookstore, and buy a book a book. a set of printed pages that are fastened inside a cover so that you can turn them and read them (書) 。 。 。 。 • Traditional knowledge based WSD are compared in a pairwise fashion. • Thus the number of computations can grow exponentially with the number of words.

4. Objectives This paper propose a new graph based method that uses the full graph of the LKB efficiently, performing better than previous approaches in English all-words datasets.

5. Methodology manage 控制住 完成 經營 應付 • Page rank • Personalized page rank

6. Methodology • Preliminary step • MCR16 + Xwn • The resulting graphhas 99, 632 vertices and 637, 290 relations. • WNet17 + Xwn • The graph has 109, 359 vertices and 620, 396edges • WNet30 + gloss • The graph has 117, 522 vertices and525, 356 relations.

7. Methodology 房屋 manage manage 完成 專案 人 專案 人 經營 房屋 應付 Personalized PageRank(Ppr) Personalized PageRank (Ppr_w2w)

8. Experiments • Dataset • Senseval2(S2AW) • Senseval3 (S3AW) • WordNet1.7 • PageRank settings: • Damping factor (c): 0.85 • End after 30 iterations

9. Experiments

10. Experiments

11. Experiments

12. Conclusions The method uses the full graph of the LKB efficiently, performing better than previous approaches in English all words datasets.

13. Comments • Advantages • … • Applications • Word sense disambiguation.