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Augmenting Wikipedia with Named Entity Tags

Augmenting Wikipedia with Named Entity Tags. Wisam Dakka Columbia University. Silviu Cucerzan Microsoft Research. IJCNLP 2008. outline. 1 Introduction 2 Related Work 3 Classifying Wikipedia Pages 4 Features Used. Independent Views 5 Challenges 6 Experiments and Findings

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Augmenting Wikipedia with Named Entity Tags

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  1. Augmenting Wikipedia with Named Entity Tags WisamDakka Columbia University SilviuCucerzan Microsoft Research IJCNLP 2008

  2. outline 1 Introduction 2 Related Work 3 Classifying Wikipedia Pages 4 Features Used. Independent Views 5 Challenges 6 Experiments and Findings 7 Conclusions and Future Work

  3. 1 Introduction

  4. 1 Introduction

  5. 1 Introduction

  6. outline 1 Introduction 2 Related Work 3 Classifying Wikipedia Pages 4 Features Used. Independent Views 5 Challenges 6 Experiments and Findings 7 Conclusions and Future Work

  7. the objective • assigning to each document in a collection one or several labels from a given set • algorithm • SVM • features • traditional • bag-of-words • Wikipedia-specific feature sets 2 Related Work

  8. outline 1 Introduction 2 Related Work 3 Classifying Wikipedia Pages 4 Features Used. Independent Views 5 Challenges 6 Experiments and Findings 7 Conclusions and Future Work

  9. Types of Wikipedia pages • Disambiguation Page (DIS) • Common Page (COMM) • Named Entity Page 3 Classifying Wikipedia Pages

  10. Entity Classes • Animated Entities (PER) • Organization Entities (ORG) • Location Entities (LOC) • Miscellaneous Entities (MISC) 3 Classifying Wikipedia Pages

  11. Animated Entities (PER) • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  12. Leonardo da Vinci • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  13. Leonhard Euler • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  14. Harry Potter • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  15. Sonny (I, robot) • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  16. Zeus • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  17. Apollo • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  18. Garfield • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  19. Alien • Human entities • real person • in fictional works • mythological deities • Non-human entities • particular animal • alien 3 Classifying Wikipedia Pages

  20. Organization Entities (ORG) • Typical examples are businesses • “Microsoft”, “Ford” • governmental bodies • “United States Congress” • non-governmental organizations • “Republican Party”, “American Bar Association” 3 Classifying Wikipedia Pages

  21. Organization Entities (ORG) • science and health units • “Massachusetts General Hospital” • sports organizations and teams • “Angolan Football Federation”, “San Francisco 49ers” • religious organizations • “Church of Christ” • entertainment organizations • “San Francisco Mime Troupe”, the rock band “The Police” 3 Classifying Wikipedia Pages

  22. Location Entities (LOC) • Geo-Political entities • “Hawaii”, “European Union”, “Australia”, and “Washington, D.C.” • Locations • “the Solar system”, “Mars”, “Hudson River”, and “Mount Rainier” • Facilities • airports, highways, streets, etc 3 Classifying Wikipedia Pages

  23. Miscellaneous Entities (MISC) • Events • “Olympic Games” • Art works • books, movies, TV programs • Artifacts • camera “Nikon D4“, the software “photoshop” • Processes • “Ettinghausen effect” • Formulas or Algorithms 3 Classifying Wikipedia Pages

  24. outline 1 Introduction 2 Related Work 3 Classifying Wikipedia Pages 4 Features Used. Independent Views 5 Challenges 6 Experiments and Findings 7 Conclusions and Future Work

  25. 4 Features Used. Independent Views

  26. 4 Features Used. Independent Views

  27. 4 Features Used. Independent Views 4.1 Page-Based Features 4.2 Context Features

  28. 4.1 Page-Based Features • Bag of Words (BOW) • Structured Data (STRUCT) • First Paragraph (FPAR) • Abstract (ABS) • Surface Forms and Disambiguations (SFD) 4 Features Used. Independent Views

  29. 4.2 Context Features • Unigram Context (UCON) • Bigram Context (BCON) 4 Features Used. Independent Views

  30. outline 1 Introduction 2 Related Work 3 Classifying Wikipedia Pages 4 Features Used. Independent Views 5 Challenges 6 Experiments and Findings 7 Conclusions and Future Work

  31. refer to entities that do not exist in Wikipedia • abstracts and structure features are only available for 68% and 79% of the pages, respectively • only had available several hundred labeled examples • feature space is very large, and many noise 5 Challenges

  32. outline 1 Introduction 2 Related Work 3 Classifying Wikipedia Pages 4 Features Used. Independent Views 5 Challenges 6 Experiments and Findings 7 Conclusions and Future Work

  33. 6 Experiments and Findings 6.1 Training Data 6.2 Classification 6.3 Results on Bag-of-words 6.4 Results on Other Feature Groups 6.5 Results for Co-training

  34. Human Judged Data (HJD) • Human Judged Data Extended (HJDE) 6.1 Training Data

  35. 6.1 Training Data

  36. 6.1 Training Data

  37. 6 Experiments and Findings 6.1 Training Data 6.2 Classification 6.3 Results on Bag-of-words 6.4 Results on Other Feature Groups 6.5 Results for Co-training

  38. algorithms • SVMs • Naïve Bayes 6.2 Classification

  39. report the results • binary classification • identify all the Wikipedia pages of type PER • 5-fold classification • PER, COM,ORG, LOC, and MISC 6.2 Classification

  40. 6 Experiments and Findings 6.1 Training Data 6.2 Classification 6.3 Results on Bag-of-words 6.4 Results on Other Feature Groups 6.5 Results for Co-training

  41. 6.3 Results on Bag-of-words

  42. 6 Experiments and Findings 6.1 Training Data 6.2 Classification 6.3 Results on Bag-of-words 6.4 Results on Other Feature Groups 6.5 Results for Co-training

  43. 6.4 Results on Other Feature Groups

  44. 6.4 Results on Other Feature Groups

  45. 6 Experiments and Findings 6.1 Training Data 6.2 Classification 6.3 Results on Bag-of-words 6.4 Results on Other Feature Groups 6.5 Results for Co-training

  46. outline 1 Introduction 2 Related Work 3 Classifying Wikipedia Pages 4 Features Used. Independent Views 5 Challenges 6 Experiments and Findings 7 Conclusions and Future Work

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