Using Wikipedia for Hierarchical Finer Categorization of Named Entities

1. Using Wikipedia for Hierarchical Finer Categorizationof Named Entities Aasish Pappu Language Technologies Institute Carnegie Mellon University PACLIC 2009

2. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 6 Conclusion

3. Structured and organized encyclopedic corpus is a suitable training corpus. • a wide range of topics • provides hyperlinks 1 Introduction

4. In this paper • Discuss the usability of Wikipedia • Induce WordNet and Wikipedia domain taxonomy into the feature space • Using Maximum Entropy and SVM classifier 1 Introduction

5. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 6 Conclusion

6. Kazama and Torisawa (2007) • extracted gloss text • Dakka and Cucerzan (2008) • tagging the Wikipedia data • Bunescu and Pasca (2006) • built a disambiguation system 2 Related Work

7. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 6 Conclusion

8. 10-18-2007 English version of Wikipedia • 2 million articles • 292,384 categories • a taxonomy with a depth about 10 • 5882 Wikipedia Stub categories • 105 domains 3 Corpus Creation

9. 3 Corpus Creation 3.1 Categories in Wikipedia 3.2 Named entity categories 3.3 Procedure

10. taxonomy • constituted by categories • linked to other categories across depth and breadth • contains cycles • Tackled by Zesch and Gurevych, 2007 • wikipedia taxonomy is not a tree 3.1 Categories in Wikipedia

11. 3 Corpus Creation 3.1 Categories in Wikipedia 3.2 Named entity categories 3.3 Procedure

12. the domain hierarchy • 17 basic domains • 88 sub-domains 3.2 Named entity categories

13. to avoid the bias towards any particular domain • rules to choose set of categories • To ensure diversity in the categorization task • To ensure we select balanced categories • consider category with each parameter closest to mean value under that domain 3.2 Named entity categories

14. 3 Corpus Creation 3.1 Categories in Wikipedia 3.2 Named entity categories 3.3 Procedure

15. extract named entity phrases • using Stanford POS tagger • extract typed dependency relationships • extract the content words around a named entity • collect the NPs (noun phrases) and VPs (verb phrases) 3.3 Procedure

16. Firstly, we look for redirected and disambiguated article titles matching with first name of the named entity. • If, there are more than one such titles, consider the target title using minimum edit distance metric. • Pick all articles that fall under the same category as the target article. • Look for those articles that fall under the special categories that are chosen for the classification task. • Find the article that shares maximum number of categories with the target article and label the target article with the its special category. 3.3 Procedure

17. About 10,000 samples • Training 75% • Testing 25% 3.3 Procedure

18. 3.3 Procedure

19. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 6 Conclusion

20. four types of feature sets • a syntactic feature set • three semantic features 4 Features

21. 4 Features 4.1 Typed Dependency Feature 4.2 Hypernyms 4.3 Domain based features

22. phrase structure parse • nesting of multi-word constituents • dependency parse • dependencies between individual words • dependency relations gives a clue about probable semantic relations that can be associated with the named entity. 4.1 Typed Dependency Feature

23. 4 Features 4.1 Typed Dependency Feature 4.2 Hypernyms 4.3 Domain based features

24. preferred to have a hypernym feature which is semantically specific • hypernyms of all synsets are inversely ordered according to their depth in the hypernymtree • deepest hypernymin the lot is choosen as the target feature for that content word 4.2 Hypernyms

25. 4 Features 4.1 Typed Dependency Feature 4.2 Hypernyms 4.3 Domain based features

26. 4 Features 4.1 Typed Dependency Feature 4.2 Hypernyms 4.3 Domain based features 4.3.1 Wordnet domains 4.3.2 Wikipedia domains 4.3.3 WDH vsWikipedia Domain System

27. Every synset in WordNet is associated a domain label in Wordnet Domain Hierarchy (WDH) • There are 5 top-level domains and 46 basic domains in WDH. 4.3.1 Wordnet domains

28. 4 Features 4.1 Typed Dependency Feature 4.2 Hypernyms 4.3 Domain based features 4.3.1 Wordnet domains 4.3.2 Wikipedia domains 4.3.3 WDH vsWikipedia Domain System

29. indexed Wikipedia • search content words in the index for the categories that contain more number of pages containing a content word • Especially, pages with links are weighed double the pages that contains the word without a hyperlink. 4.3.2 Wikipedia domains

30. 4 Features 4.1 Typed Dependency Feature 4.2 Hypernyms 4.3 Domain based features 4.3.1 Wordnet domains 4.3.2 Wikipedia domains 4.3.3 WDH vsWikipedia Domain System

31. 4.3.3 WDH vsWikipedia Domain System

32. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 6 Conclusion

33. 5 Experiments

34. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 5.1 Experiment 1: Feature wise model 5.2 Experiment 2: Feature combination model 5.3 Experiment 3: Error analysis

36. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 5.1 Experiment 1: Feature wise model 5.2 Experiment 2: Feature combination model 5.3 Experiment 3: Error analysis

40. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 5.1 Experiment 1: Feature wise model 5.2 Experiment 2: Feature combination model 5.3 Experiment 3: Error analysis

42. outline 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 6 Conclusion

43. presented a named entity categorization system • employs Wikipedia categories as classes • adapted hierachial categorization of Wikipedia • mine relations among named entities