1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Learning from Multi-topic Web Documents for Contextual Advertisement Presenter : Yu-hui Huang Authors : Yi Zhang, John C. Platt, Arun C. Surendran, Mukund Narasimhan KDD 2008 國立雲林科技大學 National Yunlin University of Science and Technology 1

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiment Conclusion

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation advertiser wants to avoid content relating to war, violence, pornography, etc. advertiser wants to detect and avoid negative opinion about their product when positive, negative and neutral sentiments co-exist on a page.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective This paper proposed a classifiers to distinguish the content whether it is positive or not for a set of advertisements and web pages

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology SENSITIVE CONTENT DETECTION ： advertising do not want their ads to be shown on web pages that contain those sensitive contents.

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology MILBoost(Multiple Instance Learning-boosting ) 黃玉輝是個帥哥, 川哥很 man 陳建興攏博班啊，已經是資深會員了 嘿阿灰啊嘸災咧衝啥，把我的獎杯都 打破了

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology MILBoost(Multiple Instance Learning-boosting )

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology MILBoost(Multiple Instance Learning-boosting )

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 11 We showed that the MILBoost system is able to improve on the performance of the traditional classifiers in such tasks, especially when the percentage of mixed content is high. Unfortunately, it do not have sentence-level labels therefore the evaluation can only be done at the page-level.

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Comments Advantage  … Drawback  …. Application  classification of web page, analyze feature of product, advertisement