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Web Taxonomy Integration through Co-Bootstrapping

Web Taxonomy Integration through Co-Bootstrapping. Dell Zhang National University of Singapore Wee Sun Lee National University of Singapore SIGIR’04. Introduction. Problem Statement. Games > Roleplaying Final Fantasy Fan Dragon Quest Home EverQuest Addict Warcraft III Clan

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Web Taxonomy Integration through Co-Bootstrapping

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  1. Web Taxonomy Integration throughCo-Bootstrapping Dell Zhang National University of Singapore Wee Sun Lee National University of Singapore SIGIR’04

  2. Introduction

  3. Problem Statement • Games > Roleplaying • Final Fantasy Fan • Dragon Quest Home • EverQuest Addict • Warcraft III Clan • Games > Strategy • Shogun: Total War • Warcraft III Clan • Games > Roleplaying • Final Fantasy Fan • Dragon Quest Home • Games > Strategy • Shogun: Total War • Games > Online • EverQuest Addict • Warcraft III Clan • Games > Single-Player • Warcraft III Clan

  4. Possible Approach Classify Train • Games > Roleplaying • Final Fantasy Fan • Dragon Quest Home • Games > Strategy • Shogun: Total War • EverQuest Addict • Warcraft III Clan ignores original Yahoo! categories

  5. Another Approach (1/2) • Use Yahoo! categories • Advantage • similar categories • Potential Problem • different structure • categories do not match exactly

  6. Another Approach (2/2) • Example: Crayon Shin-chan Entertainment > Comics and Animation > Animation > Anime > Titles > Crayon Shin-chan Arts > Animation > Anime > Titles > C > Crayon Shin-chan

  7. This Paper’s Approach • Weak Learner (as opposed to Naïve Bayes) • Boosting to combine Weak Hypotheses • New Idea: Co-Bootstrapping to exploit source categories

  8. Assumptions • Multi-category data are reduced to binary data • Totoro Fan Cartoon > My Neighbor Totoro Toys > My Neighbor Totoro is converted into Totoro Fan Cartoon > My Neighbor Totoro Totoro Fan Toys > My Neighbor Totoro • Hierarchies are ignored • Console > Sega and Console > Sega > Dreamcast are not related

  9. Weak Learner • Boosting • Co-Bootstrapping Weak Learner

  10. Weak Learner • A type of classifier similar to Naïve Bayes • + = accept • - = reject • Term may be a word or n-gram or … Weak Hypothesis (term-based classifier) After Training Weak Learner

  11. Weak Hypothesis Example • contain “Crayon Shin-chan”  • in “Comics > Crayon Shin-chan” • not in “Education > Early Childhood” • not contain “Crayon Shin-chan”  • not in “Comics > Crayon Shin-chan” • in “Education > Early Childhood”

  12. Weak Learner Inputs (1/2) • Training data are in the form [x1, y1], [x2, y2], …, [xm, ym] • xiis a document • yi is a category • [xi,yi] means document xi is in category yi • D(x, y) is a distribution over all combinations of xi and yi • D(xi, yj) indicates the “importance” of (xi, yj) • w is the term (automatically found)

  13. Weak Learner Algorithm For each possible category y, compute four values: Note: (xi,y) with greater D(xi,y) has more influence.

  14. Weak Hypothesis h(x, y) • Given unclassified document x and category y • If x contains w, then • Else if x does not contain w, then

  15. Weak Learner Comments • If sign[ h(x,y) ] = +, then x is in y • | h(x,y) |is the confidence • The term w is found as follows: • Repeatedly run weak learner for all possible w • Choose the run with the smallest value as the model • Boosting: Minimizes probability of h(x,y) having wrong sign

  16. Weak Learner • Boosting • Co-Bootstrapping Boosting (AdaBoost.MH)

  17. Boosting Idea • Train the weak learner on different Dt(x, y) distributions • After each run, adjust Dt(x, y) by putting more weight on the most often misclassified training data • Output the final hypothesis as a linear combination of weak hypotheses

  18. Boosting Algorithm Given: [x1, y1], [x2, y2], …, [xm, ym], where xi X and yi  Y Initialize D1(x,y) = 1/(mk) fort = 1,…,Tdo Pass distribution Dt to weak learner Get weak hypothesis ht(x, y) Choose t  R Update end for Output the final hypothesis

  19. Boosting Algorithm Initialization Given: [x1, y1], [x2, y2], …, [xm, ym] Initialize D(x, y) = 1/(mk) • k = total number of categories • uniform distribution

  20. Boosting Algorithm Loop fort = 1,…,Tdo • Run weak learner using distribution D • Get weak hypothesis ht(x, y) • For each possible pair (x,y) in training data • If ht(x,y) guesses incorrectly, increase D(x,y) end for return

  21. Weak Learner • Boosting • Co-Bootstrapping Co-Bootstrapping

  22. Co-Bootstrapping Idea • We want to use Yahoo! categories to increase classification accuracy

  23. Recall Example Problem • Games > Roleplaying • Final Fantasy Fan • Dragon Quest Home • Games > Strategy • Shogun: Total War • Games > Online • EverQuest Addict • Warcraft III Clan • Games > Single-Player • Warcraft III Clan

  24. Co-Bootstrapping Algorithm (1/4) • 2. Run AdaBoost on Google sites • Get classifier G1 • 1. Run AdaBoost on Yahoo! sites • Get classifierY1 • 3. Run Y1 on Google sites • Get predicted Yahoo! categories for Google sites • 4. Run G1 on Yahoo! sites • Get predicted Google categories for Yahoo! sites

  25. Co-Bootstrapping Algorithm (2/4) • 6. Run AdaBoost on Google sites • Include Yahoo! category as a feature • Get classifier G2 • 5. Run AdaBoost on Yahoo! sites • Include Google category as a feature • Get classifierY2 • 7. Run Y2 on original Google sites • get more accurate Yahoo! categories for Google sites • 8. Run G2 on original Yahoo! sites • get more accurateGoogle categories for Yahoo! sites

  26. Co-Bootstrapping Algorithm (3/4) • 10. Run AdaBoost on Google sites • Include Yahoo! category as a feature • Get classifier G3 • 9. Run AdaBoost on Yahoo! sites • Include Google category as a feature • Get classifierY3 • 11. Run Y3 on original Google sites • get even more accurate Yahoo! categories for Google sites • 12. Run G3 on original Yahoo! sites • get even more accurateGoogle categories for Yahoo! sites

  27. Co-Bootstrapping Algorithm (4/4) • Repeat, repeat, and repeat… • Hopefully, the classification will become more accurate after each iteration…

  28. Enhanced Naïve Bayes (Benchmark)

  29. Enhanced Naïve Bayes (1/2) • Given • document x • source category S of x • Predict master category C • In NB, Pr[C | x]  Pr[C] wx(Pr[w | C])n(x,w) • w : word • n(x,w) number of occurrences of w in x • Pr[C | x, S]  Pr[C | S] wx(Pr[w | C])n(x,w)

  30. Enhanced Naïve Bayes (2/2) • Pr[C] = • Estimate Pr[C | S]  • |C  S| : number of docs in S that is classified into C by NB classifier

  31. Experiment

  32. Datasets

  33. Number of Categories*/Dataset (1/2) *Top level categories only

  34. Number of Categories*/Dataset (2/2) • Book • Horror • Science Fiction • Non-fiction • Biography • History Merge into Non-fiction

  35. Number of Websites

  36. Method (1/2) • Classify Yahoo! Book websites into Google Book categories (GY) • Find GY for Book • Hide Google categories for in GY • GYYahoo! Book • Randomly take |GY| sites from G-Y Google Book

  37. Method (2/2) • For each dataset, do GY five times and GY five times • macro F-score : calculate F-score for each category, then average over all categories • micro F-score : calculate F-score on the entire dataset • recall = 100%? • Doesn’t say anything about multi-category ENB

  38. Results (1/3) • Co-Boostrapping-AdaBoost > AdaBoost macro-averaged F scores micro-averaged F scores

  39. Results (2/3) • Co-Bootstrapping-AdaBoost iteratively improves AdaBoost Book Dataset

  40. Results (3/3) • Co-Boostrapping-AdaBoost > Enhanced Naïve Bayes macro-averaged F scores micro-averaged F scores

  41. Contribution • Co-Bootstrapping improves Boosting performance • Does not require  as in ENB

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