The document discusses semi-supervised classification in natural language processing, presenting problems, methods, and comparisons between generative and discriminative models. It covers various techniques like self-training and co-training, along with specific applications for tasks like spam filtering and extracting protein interaction sentences. The results highlight the effectiveness and limitations of semi-supervised methods using different datasets.

1. SEMI-SUPERVISED CLASSIFICATION FOR
NATURAL LANGUAGE PROCESSING

2. PRESENTATION AT A GLANCE
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3. SEMI-SUPERVISED LEARNING
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4. SEMI-SUPERVISED LEARNING PROBLEMS
(1)
Learn from labeled data
Inductive
Learning
(2)
Apply learning on
unlabeled data to label
them
Transductive
Learning
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Apply learning on
unseen unlabeled data
(3)
If confident in labeling,
then learn from
(1) and (2)
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5. SEMI-SUPERVISED LEARNING PROBLEMS
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6. SCOPES OF SEMI-SUPERVISED LEARNING
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7. HOW DOES SEMI-SUPERVISED CLASSIFICATION WORK?
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8. TYPES OF SEMI-SUPERVISED LEARNING
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9. GENERATIVE VS DISCRIMINATIVE MODELS
(x,y)
Discriminative Models
Generative Models
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10. GENERATIVE VS DISCRIMINATIVE MODELS
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11. GENERATIVE VS DISCRIMINATIVE MODELS
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12. GENERATIVE VS DISCRIMINATIVE MODELS
Conditional Probability,
to determine class
boundaries
Transductive SVM,
Graph-based
methods
Joint Probability P(x,y),
for any given y, we can
generate its x
EM Algorithm,
Self-learning
Cannot be used without considering P(x)
Difficult because P(x|y) are inadequate
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13. GENERATIVE VS DISCRIMINATIVE MODELS
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14. IS THERE A FREE LUNCH?
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15. IS THERE A FREE LUNCH?
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16. IS THERE A FREE LUNCH?
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17. SELF-TRAINING
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18. CO-TRAINING
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19. CO-TRAINING
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20. CO-TRAINING
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21. CO-TRAINING
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22. CO-TRAINING: COVEATS
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23. ACTIVE LEARNING
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24. WHICH METHOD SHOULD I USE?
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25. WHICH METHOD SHOULD I USE?
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26. SEMI-SUPERVISED CLASSIFICATION FOR NLP
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27. EFFECTIVE SELF-TRAINING
FOR PARSING
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28. INTRODUCTION
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29. METHODS
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30. DATASETS
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31. RESULTS
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32. LIMITATIONS
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33. SEMI-SUPERVISED SPAM FILTERING:
DOES IT WORK?
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34. INTRODUCTION
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35. BACKGROUND
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36. BACKGROUND
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37. BACKGROUND
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38. METHODS AND MATERIALS
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39. RESULTS: DELAYED FEEDBACK VS CROSS-USER
Delayed Feedback
Cross-User
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40. RESULTS: CROSS-CORPUS
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41. EXTRACTIVE SUMMARIZATION USING
SUPERVISED AND SEMI-SUPERVISED
LEARNING
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42. INTRODUCTION
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43. METHOD
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44. DATASETS
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45. RESULTS: FEATURE SELECTION
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Human Summary ROUGE I
Score was 0.422
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46. RESULTS: EFFECT OF UNLABELED DATA
More labeled data
produced better Fscore
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47. RESULTS: SUPERVISED VS SEMI-SUPERVISED
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48. RESULTS: EFFECT OF SUMMARY LENGTH
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49. LIMITATIONS
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50. SEMI-SUPERVISED CLASSIFICATION FOR
EXTRACTING PROTEIN INTERACTION SENTENCES
USING DEPENDENCY PARSING
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51. INTRODUCTION
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52. INTRODUCTION
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53. METHOD
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54. DATASETS
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55. RESULTS: AIMED DATASET
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56. RESULTS: CB DATASET
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57. RESULTS: EFFECT OF TRAINING DATA SIZE
(AIMED)
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58. RESULTS: EFFECT OF TRAINING DATA SIZE
(CB)
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59. LIMITATIONS
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60. HOW MUCH UNLABELED DATA IS USED?
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61. CONCLUSIONS
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62. CONCLUSIONS
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63. CONCLUSIONS
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